Machines doi: 10.3390/machines12040229
Authors: Yu Tang Liang Tao Yuanqiang Li Dashan Zhang Xiaolong Zhang
The identification and control of tire side slip angle is the key to vehicle stability control. Intelligent tire technology based on the sensing of side-slip acceleration inside the tire provides a novel method for estimating the tire side-slip angle. This study proposed a method to estimate the tire side-slip angle by using the frequency domain lateral acceleration of the tire. First, an intelligent tire testing system was constructed by independently developing a special rim assembly and data collector. A three-axis accelerometer was placed on the right side of the tire, and the acceleration value was acquired by using a wired method with a sampling frequency of 50 kHz. Second, based on the constructed test system, a tire side deflection test was carried out on the Flat Trac bench. Through data analysis, it was found that the lateral acceleration was in the frequency domain of 400 Hz. As the side-slip angle increased from −4° to 4°, the vibration amplitude gradually decreased. Moreover, the vibration amplitude within 0.5~2 kHz was highly correlated with the side-slip angle. Subsequently, the vibration amplitude of the lateral acceleration within 2 kHz was extracted at an interval of 20 Hz as the feature point, and a frequency domain data set FDAy3 was established together with the vertical load and tire pressure. Finally, the support vector machine (SVM) algorithm was employed to make predictions on the data set. The grid search method was utilized to find the optimal parameter values of the model penalty factor c and radial basis kernel function coefficient g, which were 1.4142 and 0.0884, respectively. The results suggested that the root mean square error of the model prediction was 0.0806°, and the maximum estimated angle deviation of the prediction was 0.4587°. Meanwhile, the optimal prediction accuracy and real-time performance were achieved when the number of feature points and the feature frequency band were 25 and within 500 Hz, respectively. The findings of this study confirm that it is feasible to estimate the tire side-slip angle based on the frequency domain lateral acceleration of the tire, which provides a new method for tire side-slip angle estimation.
]]>Machines doi: 10.3390/machines12040228
Authors: Dae-Yong Um Seung-Ahn Chae Gwan-Soo Park
This paper has investigated a method for calculating the frequency-dependent winding resistance of toroidal inductor windings with Litz-wire as well as solid-round wire. The modified Dowell’s model is employed to address the effectiveness for inductor windings with the low and high filling factors. To overcome the limitation of this model, especially for a winding densely wound around the core, an alternative approach based on the complex permeability and iterative calculations is proposed. For the calculated AC-resistance factor of five inductors with different numbers of turns, layers with the same wire diameters are compared with that of FEA, and the three air-core toroidal windings are manufactured and tested within the frequency where the self-resonance can be neglected. The proposed model demonstrates the versality of the AC-resistance calculation of both solid- and Litz-wire windings within an error of 15% across a wide range of frequencies up to 1 MHz, compared with FEA.
]]>Machines doi: 10.3390/machines12040227
Authors: Wei-qiang Zhao Wenhui Zhao Jie Liu Na Yang
Due to complex environmental factors, the gear transmission systems of wind turbines are continuously affected by large torque load excitation with periodic and random properties. This paper shares the load-sharing and dynamic characteristics of a herringbone planetary gear system applied in a wind turbine. The gear dynamic model is established using a typical lumped parameter method, in which the nonlinear transmission errors of the gear pairs and left and right-side coupling stiffness of the herringbone gears are included. With the help of the blade element momentum theory, the precise calculation of the hub load of the wind turbine, which is the external excitation of the gear system, is implemented, in which the wind shear, tower shadow, turbulent effect, and tip loss correction are taken into consideration. The nonlinear dynamic characteristics of the system are obtained using the Runge-Kutta method and then discussed. The results show that the turbulent effect plays a major role in the impact on the load-sharing characteristics, and a reasonable set of the support stiffness of rotational components can improve the load-sharing characteristics of the system. The purpose of this research is to provide some useful references in numerical modelling and methods for designers and researchers of wind turbine transmission systems.
]]>Machines doi: 10.3390/machines12040226
Authors: Itxaso Cascón-Morán Meritxell Gómez David Fernández Alain Gil Del Val Nerea Alberdi Haizea González
Zero-Defect Manufacturing (ZDM) is a promising strategy for reducing errors in industrial processes, aligned with Industry 4.0 and digitalization, aiming to carry out processes correctly the first time. ZDM relies on digital tools, notably Artificial Intelligence (AI), to predict and prevent issues at both product and process levels. This study’s goal is to significantly reduce errors in machining large parts. It utilizes data from process models and in situ monitoring for AI-driven predictions. AI algorithms anticipate part deformation based on manufacturing data. Mechanistic models simulate milling processes, calculating tool deflection from cutting forces and assessing geometric and dimensional errors. Process monitoring provides real-time data to the models during execution. The research focuses on a high-value component from the oil and gas industry, serving as a test piece to predict geometric errors in machining based on the deviation of cutting forces using AI techniques. Specifically, an AISI 1095 steel forged flange, intentionally misaligned to introduce error, undergoes multiple milling operations, including 3-axis roughing and 5-axis finishing, with 3D scans after each stage to monitor progress and deviations. The work concludes that Support Vector Machine algorithms provide accurate results for the estimation of geometric errors from the machining forces.
]]>Machines doi: 10.3390/machines12040225
Authors: Sandi Baressi Šegota Nikola Anđelić Jelena Štifanić Zlatan Car
Motor power models are a key tool in robotics for modeling and simulations related to control and optimization. The authors collect the dataset of motor power using the ABB IRB 120 industrial robot. This paper applies a multilayer perceptron (MLP) model to the collected dataset. Before the training of MLP models, each of the variables in the dataset is evaluated using the random forest (RF) model, observing two metrics-mean decrease in impurity (MDI) and feature permutation score difference (FP). Pearson’s correlation coefficient was also applied Based on the scores of these values, a total of 15 variables, mainly static variables connected with the position and orientation of the robot, are eliminated from the dataset. The scores demonstrate that while both MLPs achieve good scores, the model trained on the pruned dataset performs better. With the model trained on the pruned dataset achieving R¯2=0.99924,σ=0.00007 and MA¯PE=0.33589,σ=0.00955, the model trained on the original, non-pruned, data achieves R¯2=0.98796,σ=0.00081 and MA¯PE=0.46895,σ=0.05636. These scores show that by eliminating the variables with a low influence from the dataset, a higher scoring model is achieved, and the created model achieves a better generalization performance across five folds used for evaluation.
]]>Machines doi: 10.3390/machines12040224
Authors: Lei Tang Li Jiang
The whole life cycle degradation data set of rolling bearings has the characteristics of large capacity, diversity, and non-stationarity. As a powerful tool for processing such time series data in deep learning algorithms, LSTM is prone to the loss of important time series information in the process of the life prediction of rolling bearings, which leads to a decline in prediction accuracy. Therefore, a method for predicting the remaining useful life (RUL) of rolling bearings based on the combination of temporal pattern attention mechanism (TPA) and LSTM is proposed. The method firstly combines hierarchical clustering and principal component analysis (PCA) to construct a multi-faceted and multi-scale preferred feature set reflecting the degradation information of rolling bearings, then strengthens the information correlation between hidden layers of the LSTM model through TPA and optimates the parameters of the fusion model of TPA and LSTM by using the gazelle optimization algorithm (GOA). Finally, the model is applied to the experimental data set of rolling bearing degradation. The results show that, compared with the traditional model, this method is more suitable for the remaining life prediction of rolling bearings.
]]>Machines doi: 10.3390/machines12040223
Authors: Ivana Bagaric Daniel Steinert Thomas Nussbaumer Johann W. Kolar
This paper presents a decoupled bearingless cross-flow fan (CFF) that operates at a supercritical speed, thereby increasing the maximum achievable rotational speed and fluid dynamic power. In magnetically levitated CFF rotors, the rotational speed and fan performance are limited by the bending resonance frequency. This is primarily defined by the low mechanical bending stiffness of the CFF blades, which are optimised for fluid dynamic performance, and the heavy rotor magnets on both rotor sides, which add significant mass but a minimal contribution to the overall rotor stiffness. This results in detrimental deformations of the CFF blades in the vicinity of the rotor bending resonance frequency; hence, the CFF is speed-limited to subcritical rotational speeds. The novel CFF rotor presented in this study features additional mechanical decoupling elements with low bending stiffness between the fan blades and the rotor magnets. Thus, the unbalance forces primarily deform the soft decoupling elements, which enables them to pass resonances without CFF blade damage and allows rotor operation in the supercritical speed region due to the self-centring effect of the rotor. The effects of the novel rotor design on the rotor dynamic behaviour are investigated by means of a mass-spring-damper model. The influence of different decoupling elements on the magnetic bearing is experimentally tested and evaluated, from which an optimised decoupled CFF rotor is derived. The final prototype enables a stable operation at 7000 rpm in the supercritical speed region. This corresponds to a rotational speed increase of 40%, resulting in a 28% higher, validated fluid flow and a 100% higher static pressure compared to the previously presented bearingless CFF without decoupling elements.
]]>Machines doi: 10.3390/machines12040222
Authors: Julien Croonen Adrien Leopold J Deraes Jarl Beckers Wim Devesse Omar Hegazy Björn Verrelst
Torque fluctuations in drivetrains are the result of dynamic excitations and can be unfavorable for the lifetime of the system. Passive ripple suppression methods exist, such as torsional dampers and flywheels, which are often bulky and not always desired. Alternatively, performant active control methods exist; however, their applicability to certain drivetrains is not covered. Therefore, this paper focuses on active control from a mechanical perspective, more specifically, drivetrain dynamics impacting active control effectiveness. A quasi-resonant controller is implemented as an active control method, and its performance and robustness are proven both in simulation on a 3-DOF mechanical model and experimentally at different excitation frequencies. The tests show that active control effectiveness is highly drivetrain-dependent. In particular, the propagation of the torque oscillation is influenced by the elastic filtering properties of the drivetrain, and the speed ripple depends on the inertial attenuation of the drivetrain. High-stiffness, low-inertia drivetrains benefit best from active control for ripple suppression because the inertial attenuation is limited, while high-stiffness elements increase the mechanical bandwidth before dynamic decoupling happens between the inertias of interest. Active control serves as a viable alternative for speed ripple reduction when drivetrain compactness is key, instead of the current passive solutions.
]]>Machines doi: 10.3390/machines12040221
Authors: Liang Su Guangchen Wang Yuan Gao Pericle Zanchetta Hengliang Zhang
For electrical machines with complex structures, the design space of parameters can be large with high dimensions during optimization. Considering the calculation cost and time consumption, it is hard to optimize all the design parameters at the same time. Therefore, in that situation, sensitivity analysis of these design parameters is usually used to sort out crucial parameters. In this paper, the sensitivity analysis-based Taguchi method is applied to optimize the axial-flux permanent magnet (AFPM) machine with yokeless and segmented armature (YASA) topology for an in-wheel traction system. According to the key parameters and their sensitivity analysis, the optimal machine scheme to meet the performance requirements can be formed. In this case study, the machine performance is improved significantly after optimization. Lastly, the experimental results verify the accuracy of the model used in this study.
]]>Machines doi: 10.3390/machines12040220
Authors: Laszlo Szamel Jackson Oloo
Switched Reluctance Motors (SRMs), Permanent Magnet Synchronous Motors (PMSMs), and induction motors may experience failures due to insulation-related breakdowns. The SRM rotor is of a non-salient nature and made of solid steel material. There are no windings on the rotor. However, the stator is composed of windings that are intricately insulated from each other using materials such as enamel wire, polymer films, mica tapes, epoxy resin, varnishes, or insulating tapes. The dielectric strength of the insulation may fail over time due to several environmental factors and processes. Dielectric breakdown of the winding insulation can be caused by rapid switching of the winding current, the presence of contaminants, and thermal aging. For reliable and efficient operation of the SRMs and other electrical machines, it is necessary to take into account the physics of the winding insulation and perform appropriate diagnostics and estimations that can monitor the integrity of the insulation. This article presents the estimation problem using a Genetic Algorithm (GA)-optimized Random Forest Regressor. Empirical properties and measurable quantities in the historical data are utilized to derive temperature and leakage current estimation. The developed model is then combined with a moving average function to increase the accuracy of prediction of the stator winding temperature and leakage current. The performance of the model is compared with that of the Feedforward Neural Network and Long Short-Term Memory over the same winding temperature and leakage current historical data. The performance metrics are based on computation of the Mean Square Error and Mean Absolute Error.
]]>Machines doi: 10.3390/machines12040219
Authors: Luis Arturo Torres-Romero Riemann Ruiz-Cruz Luis Enrique González-Jiménez
This study introduced a novel path-following controller tailored to electric vehicles equipped with a steer-by-wire system, i.e., the steering angle of the vehicle was defined by an electrical actuator. The control objective was to force the proper steering angle of the vehicle, which permits following a desired path. The system presupposed that an external algorithm that utilized sensor data provided the lateral movement references while maintaining a steady longitudinal velocity for the vehicle. The proposed control scheme was based on a robust sliding mode steering controller to manage the vehicle’s lateral movement. Furthermore, a brushless DC (BLDC) motor was considered as the steering actuator, which was controlled by a field-oriented controller (FOC), which was based on four internal proportional–integral (PI) control loops for precise steering actuation. To assess the performance of the proposed control scheme, numerical simulations were obtained, which demonstrated its effectiveness in achieving the control objective.
]]>Machines doi: 10.3390/machines12040218
Authors: Jiwei Qu Zhe Zhang Zheyu Qin Kangquan Guo Dan Li
The development of unmanned agricultural tractors (UAT) represents a significant step towards intelligent agricultural equipment. UAT technology is expected to lighten the workload of laborers and enhance the accuracy and efficiency of mechanized operations. Through the investigation of 123 relevant studies in the literature published in recent years, this article reviews three aspects of autonomous navigation technologies for UATs: perception, path planning and tracking, and motion control. The advantages and deficiencies of these technologies in the context of UATs are clarified by analyzing technical principles and the status of current research. We conduct summaries and analyses of existing unmanned navigation solutions for different application scenarios in order to identify current bottleneck issues. Based on the analysis of the applicability of autonomous navigation technologies in UATs, it can be seen that fruitful research progress has been achieved. The review also summarizes the common problems seen in current UAT technologies. The application of research to the sharing and integrating of multi-source data for autonomous navigation has so far been relatively weak. There is an urgent need for high-precision and high-stability sensing equipment. The universality of path planning methods and the efficiency and precision of path tracking need to be improved, and it is also necessary to develop highly reliable electrical control modules to enhance motion control performance. Overall, advanced sensors, high-performance intelligent algorithms, and reliable electrical control hardware are key factors in promoting the development of UAT technology.
]]>Machines doi: 10.3390/machines12040217
Authors: Vladyslav Andrusyshyn Kamil Židek Vitalii Ivanov Ján Piteľ
Nowadays, there is a worldwide demand to create new, simpler, and more intuitive methods for the manual programming of industrial robots. Gestures can allow the operator to interact with the robot more simply and naturally, as gestures are used in everyday life. The authors have developed and tested a gesture-based robot programming approach for part-handling applications. Compared to classic manual programming methods using jogging and lead-through, the gesture control method reduced wasted time by up to 70% and reduced the probability of operator error. In addition, the proposed method compares favorably with similar works in that the proposed approach allows one to write programs in the native programming language of the robot’s controller and allows the operator to control the gripper of an industrial robot.
]]>Machines doi: 10.3390/machines12040216
Authors: Shitao Chen Zhiyuan Bao Yuhong Yan Binghai Lyu Hongyu Chen Wei Hang Jinhu Wang Wenhong Zhao Julong Yuan Xu Wang
Carbide tools are extensively used in the automotive, aerospace, and marine industries. However, an unsuitable tool-edge treatment can affect the cutting performance of carbide tools. In the tool-cutting process, the cutting edge radius is one of the major factors that affect the cutting force, temperature, and quality. In this study, a cutting simulation model of carbide inserts was used to analyze the effect of the cutting edge radius on the cutting performance. The cutting edge radii of the inserts were prepared using shear-thickening polishing methods, followed by cutting experiments. The accuracy of the cutting simulation model was verified through cutting experiments. The simulation results showed that under low-speed cutting conditions, the cutting force and temperature tended to increase with an increase in the cutting edge radius, and the cutting temperature was less affected by the cutting edge radius. The results of the cutting force and cutting temperature obtained from the experiment and simulation were consistent; therefore, the cutting simulation model was verified to be reliable. The results indicate that modeling cutting simulation is a promising research method for predicting the cutting performance of tools.
]]>Machines doi: 10.3390/machines12040215
Authors: Xiaona Shi Kelong Wang Guochao Li Chenghao Lyu Lei Zhao Jianzhi Chen Li Sun Hengheng Wu
This paper focuses on the study of the induction heating process of a camshaft in a marine diesel engine. A three-dimensional finite element model for dynamic induction heating is established using the finite element method of multi-physical field coupling, aiming to investigate the temperature uniformity of the cam during this process. Three elements are analyzed in this study: the moving speed, the gap between the induction coil and the workpiece, and the width of the induction coil. These factors allow for an analysis of the temperature distribution in the thickness direction and contour line direction of the cam under various conditions. On this basis, an equivalent parameter about the temperature uniformity in the thickness direction of the cam is proposed to guide the selection of the camshaft induction heating process parameters.
]]>Machines doi: 10.3390/machines12040214
Authors: Tamás Dózsa Péter Őri Mátyás Szabari Ernő Simonyi Alexandros Soumelidis István Lakatos
In this work we propose proof-of-concept methods to detect malfunctions of the braking system in passenger vehicles. In particular, we investigate the problem of detecting deformations of the brake disc based on data recorded by acceleration sensors mounted on the suspension of the vehicle. Our core hypothesis is that these signals contain vibrations caused by brake disc deformation. Since faults of this kind are typically monitored by the driver of the vehicle, the development of automatic fault-detection systems becomes more important with the rise of autonomous driving. In addition, the new brake boosters separate the brake pedal from the hydraulic system which results in less significant effects on the brake pedal force. Our paper offers two important contributions. Firstly, we provide a detailed description of our novel measurement scheme, the type and placement of the used sensors, signal acquisition and data characteristics. Then, in the second part of our paper we detail mathematically justified signal representations and different algorithms to distinguish between deformed and normal brake discs. For the proper understanding of the phenomenon, different brake discs were used with measured runout values. Since, in addition to brake disc deformation, the vibrations recorded by our accelerometers are nonlinearly dependent on a number of factors (such as the velocity, suspension, tire pressure, etc.), data-driven models are considered. Through experiments, we show that the proposed methods can be used to recognize faults in the braking system caused by brake disc deformation.
]]>Machines doi: 10.3390/machines12040213
Authors: Yuan Lin Xiao Liu Zishun Zheng
This study focuses on a crucial task in the field of autonomous driving, autonomous lane change. Autonomous lane change plays a pivotal role in improving traffic flow, alleviating driver burden, and reducing the risk of traffic accidents. However, due to the complexity and uncertainty of lane-change scenarios, the functionality of autonomous lane change still faces challenges. In this research, we conducted autonomous lane-change simulations using both deep reinforcement learning (DRL) and model predictive control (MPC). Specifically, we used the parameterized soft actor–critic (PASAC) algorithm to train a DRL-based lane-change strategy to output both discrete lane-change decisions and continuous longitudinal vehicle acceleration. We also used MPC for lane selection based on the smallest predictive car-following costs for the different lanes. For the first time, we compared the performance of DRL and MPC in the context of lane-change decisions. The simulation results indicated that, under the same reward/cost function and traffic flow, both MPC and PASAC achieved a collision rate of 0%. PASAC demonstrated a comparable performance to MPC in terms of average rewards/costs and vehicle speeds.
]]>Machines doi: 10.3390/machines12040212
Authors: Tomoya Hoshina Takato Yamada Mingcong Deng
This paper aims to achieve precise position control of a stage used in semiconductor exposure apparatus. The demand for smart devices, such as smartphones, is rapidly expanding, and their performance is expected to continue to improve. To manufacture these devices, it is necessary to miniaturize semiconductor devices and improve productivity. The precise control of semiconductor exposure apparatus is important for the manufacture of ultra-small semiconductor devices. The stage of semiconductor exposure apparatus uses a linear motor, and this paper performs high-precision perfect tracking control of this stage. Perfect tracking control is a control method that always follows the command value while the command value changes moment by moment, and requires high accuracy. In high-precision positioning, uncertainty in the stage model has a significant impact. Therefore, this paper proposes a method to reduce tracking errors due to the influence of uncertainty by performing uncertainty compensation using sliding mode control with the estimated value of uncertainty. The estimation of uncertainty uses a method that combines Kernel LMS with an observer. Instead of the widely used Gaussian kernel, this paper uses a generalized Gaussian kernel that allows for finer parameter settings. Furthermore, this paper proposes a method to adaptively optimize the shape parameter of the generalized Gaussian kernel. Our simulations and experiments confirm that the proposed method improves tracking performance compared to conventional sliding mode control.
]]>Machines doi: 10.3390/machines12030211
Authors: Vlad Andrei Ciubotariu Cosmin Constantin Grigoras Valentin Zichil Bogdan Alexandru Chirita
To address diverse challenges and accelerate the adoption of PV technology, innovative and cost-effective PV assemblies are essential. The Analysis of Interconnected Decision Areas—the AIDA method—offers a promising approach to achieving this goal by providing a structured framework for identifying, assessing, and optimizing the design of PV assemblies. The aim is to demonstrate how AIDA can be effectively used to identify and assess potential improvements in PV assembly design, leading to the development of more efficient, cost-effective, and environmentally friendly PV systems. For this, out of 54 combinations, 10 of them were retained, so that in the end only two possible solutions obtained by applying AIDA remained. Both structures were assessed by applying FEM, analysing data regarding equivalent von Mises stresses and displacements but also the existence of stress hotspots. A design insight study was also carried out. Also, the models were first built by additive manufacturing (3D printing). These models were evaluated by a manufacturer so that the evaluation matrix and criteria satisfaction matrix could be successfully completed. Therefore, AIDA can be successfully used in solving problems in product design in the field of mounting structures for PV panels. Depending on the manufacturer’s capabilities, the intended functions can be adapted quickly, because AIDA is quite simple to apply if the data of the problem are known very well. Following the application of the FEM it was concluded that the surfaces as simple as possible are to be followed in the design of components. Also, an assessment of environmental impact was successfully undertaken by means of software assistance. The decision to use one option or another is a subjective one. If the technical data are followed, then one type of structure is the one that the manufacturer should adopt as a solution to the problem. However, if the manufacturer considers that the impact on the environment is important and dedicates resources in this direction, then a different type of structure should be adopted.
]]>Machines doi: 10.3390/machines12030210
Authors: Xiaoning Bai Yonghua Li Dongxu Zhang Zhiyang Zhang
The paper analyzes the correlation features between stress strength, multiple failure mechanisms, and multiple components. It investigates the effects of different correlation features on reliability and proposes a method for structural reliability analysis that considers the joint effects of multiple correlation features. To portray the stress–strength correlation structure, the Copula function is utilized and the influence of the correlation degree parameter on reliability is clarified. The text describes the introduction of time-varying characteristics of structural strength and correlation parameters. A time-varying Copula is then constructed to calculate the structural reliability under the stress–strength correlation characteristics. Additionally, a time-varying hybrid Copula is constructed to characterize the intricate and correlation features of multiple failure mechanisms and components. The article proposes the variational adaptive sparrow search algorithm (VASSA) to obtain optimal parameters for the time-varying hybrid Copula. The effectiveness and accuracy of the proposed method are verified through actual cases. The results indicate that multiple correlation features significantly influence structural reliability. Incorporating multiple correlation features into the solution of structural reliability yields safer results that align with engineering practice.
]]>Machines doi: 10.3390/machines12030209
Authors: Zhiyuan Lv Pengfei Liu Donghong Ning Shuqing Wang
It is essential to ensure stability during marine transportation or the installation of high center of gravity loads. The heavy loads increase gravity disturbance, affecting the steady-state-error control of the multiple degrees of freedom (DOFs) motion compensation platform. In this paper, we propose a proportional derivative (PD) controller with dynamic gravity compensation (PDGC) for a 3-RCU (revolute–cylindrical–universal) parallel platform to improve the control effect of marine motion compensation for high center of gravity loads. We introduce an evaluation parameter of load stability and a weighting coefficient of anti-swaying control to tune the controller performance. The controller can set its control target between the two, keeping the load contact surface level and allowing the load center of gravity with the least movement. By deriving the Jacobian matrix, the gravity disturbance in the joint space is calculated and is compensated in the controller. First, we verify the control superiority of this controller over the PD controller under sinusoidal excitation in simulation and validate the effectiveness of the proposed anti-swing strategy. Then, the experiments are conducted with random excitation. The root mean square (RMS) value of the load’s residual angle with the proposed controller is reduced to 32.2% and 17.6% in two directions, respectively, compared with the PD controller under class 4 sea state excitation. The proposed method is effective for the anti-swaying control of ship-mounted 3-RCU parallel platforms.
]]>Machines doi: 10.3390/machines12030208
Authors: Jia-Mei Nie Xiang-Bo Liu Xiao-Liang Zhang
Mechanical memory elements cannot be accurately modeled using the Lagrangian method in the classical sense, since these elements are nonconservative in the plane of their non-constitutive relationships, and the system differential equations are not self-adjoint and therefore do not allow a Lagrangian formulation. To overcome this problem, the integrated Lagrangian modeling method is introduced, in which the associated conventional energies in the system are replaced by the corresponding memory state functions of the memory elements. An example, a vehicle shimmy system equipped with fluid mem-inerters, is presented to verify the improvement of modeling accuracy of mechanical systems with memory elements via the integrated Lagrangian method. The simulation results show that under pulse and random excitation, using the Lagrangian method to model the system, the values of system response indicators exhibit significant errors ranging from 5.17% to 24.54% compared with the values obtained by the integrated Lagrangian method, namely, the accurate values. In addition, the influencing factors of the error and are discussed and the fractional-order memory elements and their modeling are also briefly generalized.
]]>Machines doi: 10.3390/machines12030207
Authors: Marta Zamorano María Jesús Gómez Cristina Castejon
New trends in maintenance techniques are oriented to digitization and prognosis. The new electronic devices based on IoT (Internet of Things) technology among others that support the industry 4.0 paradigm let enhance the traditional condition monitoring techniques to better understand and predict the state of a machine in service. Related to maintenance applications, one of the important steps in condition monitoring tasks for fault diagnosis is the selection of the optimal pattern to provide accurate results (avoiding fault positives/negatives) with adequate computation time. When implementing this, the selection of optimal parameters and thresholds for setting alarms are important to detect problems in the machine before the failure occurs. Vibratory signals have been proved to be a good variable to determine their mechanical behavior. Nevertheless, parameters obtained from time domain measurements are not computationally efficient nor good patterns to compare different machine conditions. In this sense, tools that represent the frequency domain or time–frequency domain have been useful to detect defects in rotating elements such as bearings. In this work, defects in ball bearings are studied using wavelet packet transform. For this, a methodology will be developed for the optimal selection of the mother wavelet, incorporating intelligent classification systems, and using a medium Gaussian support vector machine model. In this way, it will be verified that the correct selection of this function influences both the results and the ease and reliability of detection. The results using the selected mother wavelet will be compared to those using Daubechies 6, since it is the mother wavelet that has been used in previous works and which was selected based on experience. For it, vibratory signals are obtained from a testbench with different bearing conditions: healthy bearings and defective bearings (inner and outer race).
]]>Machines doi: 10.3390/machines12030206
Authors: Yeda Wang Xiaoping Liao Juan Lu Junyan Ma
To address the issues of workpiece distortion and excessive material melting caused by heat accumulation during laser cutting of thin-walled sheet metal components, this paper proposes a segmented optimization method for process parameters in sheet metal laser cutting considering thermal effects. The method focuses on predetermined perforation points and machining paths. Firstly, an innovative temperature prediction model Tpr,t is established for the nth perforation point during the cutting process, with a prediction error of less than 10%. Secondly, using the PSO-BP-constructed prediction model for laser cutting quality features and an empirical model for processing efficiency features, a multi-objective model for quality and efficiency is generated. The NSGA II algorithm is employed to solve the objective optimization model and obtain the Pareto front. Next, based on the predicted temperature at the perforation point using the model Tpr,t, the TOPSIS decision-making method is applied. Different weights for quality and efficiency are set during the cutting stages where the temperature is below the lower threshold and above the upper threshold. Various combinations of machining parameters are selected, and by switching the parameters during the cutting process, the thermal accumulation (i.e., temperature) during processing is controlled within a given range. Finally, the effectiveness of the proposed approach is verified through actual machining experiments.
]]>Machines doi: 10.3390/machines12030205
Authors: Muhammad Nasir Ananda Maiti
In this paper, an adaptive and resilient consensus control mechanism for multi-robot systems under Byzantine attack, based on sliding mode control, is proposed. The primary aim of the article is to develop a finite-time consensus control strategy even in the presence of a Byzantine attack. In the start, a finite-time consensus control mechanism is proposed to identify the essential conditions required for ensuring consensus accuracy in multi-robot systems, even when faced with Byzantine attacks using Lyapunov theory. Subsequently, a sliding mode control is combined with an adaptive technique for multi-robot systems that lack prior knowledge of Byzantine attack. Later, an attack observer is proposed to estimate the performance of multi-robot systems in the presence of a Byzantine attack. Additionally, chattering effects are mitigated by employing integral sliding mode control. As a result, resilient consensus performance of multi-robot systems can be achieved in a finite time interval. A simulation example is also presented to validate the effectiveness of the proposed model. Furthermore, we delve into the data structure of the proposed method and explore its integration with Artificial Intelligence for seamless incorporation into the Industrial Internet of Things applications.
]]>Machines doi: 10.3390/machines12030204
Authors: Liping Zeng Zihao Wan Gang Li
The vibration frequency characteristics of a rotor system are directly related to its moment of inertia. In this paper, a moment of inertia adjustment device is proposed to adjust the frequency characteristics of a rotor system and better reduce vibration by changing the moment of inertia. First, a mathematical model of the moment of inertia and the temperature field are established. A finite element simulation model of the electromagnetic field of the electromagnetic control unit in the device is established. The influence of current and air gap on the electromagnetic forces is discussed. Then, the validity of the finite element simulation for the electromagnetic control unit is verified using experimental results. In addition, the variations in the displacement and force of the moving mass and the moment of inertia of the device with speed are analyzed. The results show that the proposed moment of inertia adjustment device can be used to significantly adjust the moment of inertia, which provides a reference for better controlling vibrations in rotor systems. Finally, a finite element simulation model for an electromagnetic field analysis of the electromagnetic control unit in the device is established. The results show that the maximum temperature of the electromagnetic control device is 332 K in 60 min, which is in accordance with the requirements.
]]>Machines doi: 10.3390/machines12030203
Authors: Michal Hovanec Peter Korba Samer Al-Rabeei Martin Vencel Branislav Racek
This paper presents the development and use of digital tools in the maintenance processes and ergonomics of work systems in the aerospace industry. The Industry 4.0 strategy aims to ensure the reliability of the human factor throughout the entire lifecycle of the maintenance process in the aerospace industry. Based on the requirement placed on the digital model of the working environment obtained from the 3D scanner data, an advanced software solution from TECNOMATIX, namely the TX JACK software 16.1.0 module, was used. The investigated digital ergonomic model, with two variants of workers with anthropometrically different weights, is the subject of analysis and simulation of the maintenance work process in an aerospace organization. Furthermore, the research also shows how the workers of maintenance and repair organizations are willing to develop their own knowledge and skills. The aviation industry should invest in the development of reliable software and hardware, improve safety at the level of digital ergonomics and the quality of jobs involving digitalization, and offer appropriate training for safety and quality personnel. The aim of this paper is to ensure the reliability of the human factor in the maintenance process and, consequently, to ensure technical safety by means of innovative tools in practice. The findings suggest that the investigated TESTER-STEND model with high-end adjustable pistons will improve ergonomics, worker performance, and work safety as a whole.
]]>Machines doi: 10.3390/machines12030202
Authors: Yujin Shin Chanhee Lee Euiho Kim
Real-time kinematic (RTK) positioning of the global navigation satellite systems (GNSS) is used to provide centimeter-level positioning accuracy. There are several ways to implement RTK but a Kalman filter-based RTK is preferred because of its superior capability to resolve GNSS carrier phase integer ambiguities. However, the positioning performance of the Kalman filter-based RTK is often compromised by various factors when it comes to determining a precise relative position vector between moving unmanned aerial vehicles (UAVs) equipped with low-cost GNSS receivers and antennas, where the locations of both GNSS antennas are not accurately known and change over time. Some of the critical factors that lead to a high rate of incorrect resolutions of carrier phase integer ambiguities are measurement time differences between GNSS receivers, frequent cycle slips with high noise in code and carrier phase measurements, and an improper Kalman filter gain due to a newly risen satellite. In this paper, effective methods to deal with those factors to achieve a seamless Kalman filter-based RTK performance in moving UAVs are presented. Using our extensive 45 flight tests data sets, conducted over a duration of 3 to 12 min, the RTK positioning results showed that the root-mean-square position error (RMSE) decreased by up to 95.13%, with an average of 65.31%, and that the percentage of epochs that passed the ratio test, which is the most common method for validating double differenced carrier phase integer ambiguity resolution, increased by up to 130%, with an average of 23.54%.
]]>Machines doi: 10.3390/machines12030201
Authors: Shihao Pan Ting Wang Haoran Zhang Tao Li
This paper studies the trajectory tracking anti-disturbance control of unmanned autonomous helicopters (UAHs) under matched disturbances and mismatched ones. Firstly, the six-degrees-of-freedom UAH nonlinear system is simplified via feedback linearization to handle strong coupling, in which the multiple disturbances are composed of modeled disturbances and time-varying bounded ones. Secondly, in order to estimate these disturbances, a new design method of a composite disturbance observer is proposed. On the one hand, for the mismatched disturbances, a normal disturbance observer (DO) combined with a backstepping control are utilized to handle their negative effect. On the other hand, two refined disturbance observers (RDOs) are constructed to estimate the matched disturbances, in which the coupling estimations are involved. Then, by designing two anti-disturbance composite controllers, the boundedness of the tracking errors is guaranteed by using the Lyapunov stability theory. Finally, some numerical simulations are provided to demonstrate the effectiveness and advantage of the proposed control scheme.
]]>Machines doi: 10.3390/machines12030200
Authors: Gabriel G. R. de Castro Tatiana M. B. Santos Fabio A. A. Andrade José Lima Diego B. Haddad Leonardo de M. Honório Milena F. Pinto
This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.
]]>Machines doi: 10.3390/machines12030199
Authors: Alessandro Giorgetti Filippo Ceccanti Niccolò Baldi Simon Kemble Gabriele Arcidiacono Paolo Citti
Powder Bed Fusion Laser Melting (PBF-LM) additive manufacturing technology is expected to have a remarkable impact on the industrial setting, making possible the realization of a metallic component with very complex designs to enhance product performance. However, the industrial use of the PBF-LM system needs a capability monitoring system to ensure product quality. Among the various studies developed, the investigation of methodology for the actual machine capability determination has been faced and still represents an open point. There are multiple authors and institutes proposing different investigation methods, ranging from the realization of samples (ex situ analysis) to installing monitoring devices on the machine (in situ analysis). Compared to other approaches, sample realization allows for assessing how the machine works through specimen analysis, but it is sensitive to the sample design. In this article, we first present an analysis of a well-known test artifact from an Axiomatic Design perspective. Second, based on the customer needs analysis and adjustments with respect to the use of hypothetical additive production lines, a new test artifact with an uncoupled design matrix is introduced. The proposed design has been experimentally tested and characterized using artifact made of Inconel 718 superalloy to evaluate its performance and representativeness in machine capability assessment. The results show an accurate identification of beam offset and scaling factor considering all the building platform positions. In addition, the artifact is characterized by a reduced building time (more than 90% with respect to the reference NIST artifact) and a halved inspection time (from 16 h to 8 h).
]]>Machines doi: 10.3390/machines12030198
Authors: Xiangyu Zhang Bowen Xie Yang Yang Yongbin Liu Pan Jiang
The wheeled chassis, which is the carrying device of the existing handling robot, is mostly only suitable for flat indoor environments and does not have the ability to work on outdoor rugged terrain, greatly limiting the development of chassis driven handling robots. On this basis, this paper innovatively designs a four-wheel-driving Ackerman chassis with strong vibration absorption and obstacle surmounting capabilities and conducts performance research and optimization on it through quantitative experiments and dynamic simulation. Firstly, based on the introduction of the working principle and structure of the four-wheel-driving Ackerman carrier chassis, a multi-sensor distributed dynamic performance test system is constructed through the analysis of the chassis performance evaluation index. Then, according to the quantitative operation experiment of the chassis, the vibration and acceleration characteristics of the chassis at different positions of the chassis, the amount of slip and straightness of the chassis under different running distance, and the operating characteristics of the chassis under different road conditions and different damping springs conditions were analyzed respectively, which verified the rationality of the chassis design. Finally, by constructing the chassis dynamics simulation model; the influence law of chassis structure; and performance parameters such as chassis wheelbase, guide rod structure, and parameters, wheel friction coefficient and assembly error on the dynamic characteristics of the chassis is studied, and the optimal structure of the four-wheel-driving Ackerman chassis is determined while it is verified based on the simulation results. The research shows that the four-wheel-driving Ackerman chassis has good vibration performance and stability and has strong adaptability to different roads. After optimization, the vibration performance, stability, amount of slip, and straightness of the chassis structure are significantly improved, and the straightness is reduced to 0.399%, which is suitable for precise carriage applications on the chassis. The research has important guiding significance for promoting the development and application of wheeled chassis.
]]>Machines doi: 10.3390/machines12030197
Authors: Cunxiang He Yufei Liu Yuhan Liu
Having emerged as strategic focal points in industrial transformation and technological innovation, intelligent machine tools are pivotal in the field of intelligent manufacturing. Accurately forecasting emerging technologies within this domain is crucial for guiding intelligent manufacturing’s evolution and fostering rapid innovation. However, prevailing research methodologies exhibit limitations, often concentrating on popular topics at the expense of lesser-known yet significant areas, thereby impacting the accurate identification of research priorities. The complex, systemic, and interdisciplinary nature of intelligent machine tool technology challenges traditional research approaches, particularly in assessing technological maturity and intricate interactions. To overcome these challenges, we propose a novel framework that leverages technological communities for a comprehensive analysis. This approach clusters data into specific topics which are reflective of the technology system, facilitating detailed investigations within each area. By refining community analysis methods and integrating structural and interactive community features, our framework significantly improves the precision of emerging technology predictions. Our research not only validates the framework but also projects key emerging technologies in intelligent machine tools, offering valuable insights for business leaders and scholars alike.
]]>Machines doi: 10.3390/machines12030196
Authors: Mirco Polonara Alessandra Romagnoli Gianfranco Biancini Luca Carbonari
Incorporating collaborative applications constitutes a challenging and increasingly intricate objective within the context of small and medium-sized enterprises (SMEs). This challenge stems from the shortage of highly specialized personnel in these types of companies when it comes to adopting cutting-edge technologies. The lack of innovation in production processes, however, increases the risk that SMEs will not be able to adapt to rapid changes in the market and the growing needs of consumers, who today are evolving at an unprecedented pace. The importance of adopting collaborative applications can be found in their capacity to harmonize human adaptability with the precision of robotic technology. This synergy contributes to the establishment of a safer work environment while guaranteeing effective and efficient performance. These features not only lead to improved production line performance compared to traditional manual or stationary operations, but also highlight new perspectives in the design, production, and customization of new products. This, in turn, helps companies strengthen their competitiveness in the global market. In this scenario, the primary challenge centers around effectively putting these solutions into practice. Our research aims to highlight how significant benefits can be achieved, both in terms of performance improvements and economically, through the analysis of a simple yet illuminating case study.
]]>Machines doi: 10.3390/machines12030195
Authors: Andreas Dörner Marek Bures Michal Simon Gerald Pirkl
Cognitive ergonomics and the mental health of production workers have attracted increasing interest in industrial companies. However, there is still not much research available as it is regarding physical ergonomics and muscular load. This paper designs an experiment to analyze the cognitive ergonomics and mental stress of shop floor production workers interacting with different user interfaces of a Manufacturing Execution System (MES) that is adjustable for analyzing the influence of other assistive systems, too. This approach is going to be designed with the Design of Experiments (DoE) method. Therefore, the respective goals and factors are going to be determined. The environment will be the laboratories of the University of Applied Sciences Amberg-Weiden and its Campus for Digitalization in Amberg. In detail, there will be a sample assembly process from the automotive supplier industry for demonstration purposes. At this laboratory, the MES software from the European benchmark SAP is installed, and the respective standard Production Operator Desk is going to be used with slight adaptions. In order to make the cognitive ergonomics measurable, different approaches are going to be used. For instance, body temperature, heart rate and skin conductance as well as subjective methods of self-assessment are planned. The result of this paper is a ready-to-run experiment with sample data for each classification of participants. Further, possible limitations and adjustments are going to be discussed. Finally, an approach to validating the expected results is going to be shown and future intentions are going to be discussed.
]]>Machines doi: 10.3390/machines12030194
Authors: Milos Knezev Robert Cep Luka Mejic Branislav Popovic Aco Antic Branko Strbac Aleksandar Zivkovic
Understanding the temperature–working condition relationship is crucial for optimizing machining processes to ensure dimensional accuracy, surface finish quality, and overall spindle longevity. Monitoring and controlling spindle temperature through appropriate cooling systems and operational parameters are essential for efficient and reliable machining operations. This paper presents an in-depth analysis of the thermal equilibrium and deformation characteristics of a high-speed motorized spindle unit utilized in grinding machine tools. Through a series of thermal equilibrium experiments and meticulous data acquisition, the study investigates the nuanced influence of various working conditions, including spindle speeds, coolant types, and coolant flow rates, on spindle temperatures and thermal deformations. Leveraging the power of Artificial Neural Networks (ANNs), predictive models are meticulously developed to accurately forecast spindle behavior. Subsequently, the models are seamlessly transitioned to a cloud computing infrastructure to ensure remote accessibility and scalability, facilitating real-time monitoring and forecasting of spindle performance. The validity and reliability of the predictive models are rigorously assessed through comparison with experimental data, demonstrating excellent agreement and high accuracy in forecasting spindle thermal behavior. Furthermore, the study underscores the critical role of key working condition variables as precise predictors of spindle temperature and thermal deformation, emphasizing their significance in optimizing overall spindle efficiency and performance. This comprehensive analysis offers valuable insights and practical implications for enhancing spindle operation and advancing the field of grinding machine tools.
]]>Machines doi: 10.3390/machines12030193
Authors: Dehuang Gong Xueqian Wei Hongli Liu Fengming Li
A satellite with two solar wings can be modeled using a pair of symmetric flexible cantilever beams connected to a central rigid body. Due to certain reasons, the symmetric flexible cantilever beams may be turned into asymmetric ones, which will inevitably influence the vibration properties of the structural system. By changing the structural sizes and adding local mass on one side of the two beams, a structural system with asymmetric mass distribution is obtained and its vibration characteristics are investigated. Hamilton’s principle with the assumed mode method is employed to establish the equation of motion of the asymmetric structural system. The natural frequencies, mode shapes, frequency response curves and displacement time histories of the system are calculated, and they are compared with those of the structural system with a symmetric mass distribution. The correctness and feasibility of the present analytical method are verified by means of the finite element method (FEM) and a vibration experiment. The analytical results show that the mass asymmetry of the two beams leads to the mode localization phenomenon, and the coupling effect between the two beams and the central rigid body is enhanced. The larger the mass asymmetry is and the closer the position of the added local mass to the end of the cantilever beam is, the more obvious of the mode localization phenomenon is and the more obvious of the coupling effect between the two beams and the central rigid body is. The present investigation results are helpful for the dynamic analysis and design of spacecraft structures composed of flexible solar wings and a central rigid body.
]]>Machines doi: 10.3390/machines12030192
Authors: Abdulmajeed Dabwan Husam Kaid Abdulrahman Al-Ahmari Khaled N. Alqahtani Wadea Ameen
The dynamic scheduling problem (DSP) in unreliable flexible manufacturing systems (UFMSs) with concurrency, conflicts, resource sharing, and sequential operations is a complex optimization problem that requires the use of efficient solution methodologies. The effectiveness of scheduling UFMSs relies on the quality of equipment maintenance. Currently, UFMSs with consistently large queues of parts awaiting service employ a repair-after-failure approach as a standard maintenance procedure. This method may require unexpected resources, incur costs, consume time, and potentially disrupt the operations of other UFMSs, either partially or fully. This study suggests using a predictive maintenance (PdM) strategy that utilizes the Internet of Things (IoT) to predict and avoid early mechanical equipment failures before they happen in UFMSs, thereby reducing unplanned downtime and enhancing reliability. Therefore, the objective of this paper is to construct timed Petri net (TPN) models using the IoT for the PdM configuration of mechanical equipment in the dynamic scheduling problem of UFMSs. This necessitates that users represent the specific problem using TPNs. The process of PN modeling requires the utilization of domain knowledge pertaining to the target problems as well as to machine information. However, it is important to note that the modeling rules for PNs are straightforward and limited in number. Consequently, the TPN model is applied to generate and formulate mixed-integer linear programming (MILP) instances accurately. This is done to identify the optimal production cycle time, which may be implemented in real-life scenarios. Several UFMS instances are used to demonstrate the applications and effectiveness of the proposed method. The computational results demonstrate that the proposed method shows superior solution quality, effectively solves instances for a total of 10 parts and 6 machines, and achieves a solution in a reasonable CPU time.
]]>Machines doi: 10.3390/machines12030191
Authors: Mingjie Feng Jianbo Dai Wenbo Zhou Haozhi Xu Zhongbin Wang
Given the difficulty in manually adjusting the position and posture of the pile body during the pile driving process, the improved Denavit-Hartenberg (D-H) parameter method is used to establish the kinematics equation of the mechanical arm, based on the motion characteristics of each mechanism of the mechanical arm of the pile driver, and forward and inverse kinematics analysis is carried out to solve the equation. The mechanical arm of the pile driver is modeled and simulated using the Robotics Toolbox of MATLAB to verify the proposed kinematics model of the mechanical arm of the pile driver. The Monte Carlo method is used to investigate the working space of the mechanical arm of the pile driver, revealing that the arm can extend from the nearest point by 900 mm to the furthest extension of 1800 mm. The actuator’s lowest point allows for a descent of 1000 mm and an ascent of up to 1500 mm. A novel multi-strategy grey wolf optimizer (GWO) algorithm is proposed for robotic arm three-dimensional (3D) path planning, successfully outperforming the basic GWO, ant colony algorithm (ACA), genetic algorithm (GA), and artificial fish swarm algorithm (AFSA) in simulation experiments. Comparative results show that the proposed algorithm efficiently searches for optimal paths, avoiding obstacles with shorter lengths. In robotic arm simulations, the multi-strategy GWO reduces path length by 16.575% and running time by 9.452% compared to the basic GWO algorithm.
]]>Machines doi: 10.3390/machines12030190
Authors: Di Yuan Dong Wang Qiang Wan
A novel penalty contact constitution was developed to replicate the hysteresis memory effect observed in contact interfaces. On this basis, a refined finite element analysis (FEA) was performed to study the stick–slip friction contact behavior of bolted joint interfaces. The analysis was validated by comparing it with the experimental hysteresis loops in the literature. The simulated hysteresis loops were subsequently used to identify four parameters of the Iwan model. Additionally, the effects of bolt clamping, friction coefficient, and excitation amplitude were individually examined. It was found that the deterioration in bolt clamping performance resulted in a decrease in both the equivalent joint stiffness and energy dissipation. Similarly, the reduction in the friction coefficient yielded a comparable impact. Furthermore, the identified model parameters of critical stick–slip force and displacement exhibited a quasi-linear relationship to the bolt preload and friction coefficient.
]]>Machines doi: 10.3390/machines12030189
Authors: Michal Bučko Lucie Krejčí Ivo Hlavatý Jiří Lorenčík
Businesses are constantly trying to improve their production by looking for bottlenecks to improve their market position. The introduction and innovation of automated production lines is necessary for both labor shortages and productivity and quality reasons. A combination of precision, fluidity, and speed, that is the basic definition of a production line. With the advent of new technologies, production lines have also begun to continuously speed up and innovate. Innovation is the subject of this paper, where the problem of designing a completely new layout for a new production line in the food industry has been addressed. The aim of this paper was to create a design for the optimal layout of the production line in preselected production areas. Optimal use of the space allocated for production is very important for every company today.
]]>Machines doi: 10.3390/machines12030188
Authors: Petr Baron Oleksandr Pivtorak Ján Ivan Marek Kočiško
The present paper describes a study conducted at the request of the operator of machining center equipment. The operator observed undesirable indicators in terms of increased backlash and vibration of the milling head and poor quality of the machined surfaces. Vibration measurements and vibrodiagnostics were carried out before disassembling the milling head in the idle state. The bearings, lubricant, and friction regime were analyzed in the next step. The vibrodiagnostic methods used included VEL, ACC, EN2, EN3, and EN4, with recommended limits conforming to STN ISO 10816-3. The vibration values obtained indicated a problem with the bearings, exceeding the limit values. After disassembly of the bearings, abrasive wear, corrosion, and improper lubricant conditions were detected. Lubricant analysis showed the presence of abrasive and corrosive particles, indicating an unsatisfactory friction regime. Determining the optimum lubricant temperature and the effect on friction torque constituted other aspects of the study. Inspection of the bearing microgeometry confirmed unsatisfactory roundness. Furthermore, the assembly of tapered roller bearings with axial preload was analyzed with a focus on bearing stiffness, accuracy, and life. The results showed that preload improves shaft guidance accuracy and load distribution, promoting reliable operation and extending bearing life.
]]>Machines doi: 10.3390/machines12030187
Authors: Anurag Balayan Rajnish Mallick Stuti Dwivedi Sahaj Saxena Bisheshwar Haorongbam Anshul Sharma
This research addresses the imperative challenge of a lightweight design for an Unmanned Aerial Vehicle (UAV) chassis to enhance the thrust-to-weight and power-to-weight ratios, crucial for optimal flight performance, focused on developing an intriguing lightweight yet robust quadcopter chassis. Advanced generative design techniques, integrated with topology optimization, using Autodesk Fusion 360 software (v. 16.5. 0.2083), 3D-printing methods and lightweight materials like Polylactic Acid (P.L.A.), Acrylonitrile Butadiene Styrene (A.B.S.), and Nylon 6/6 play a significant role in achieving the desired balance between structural integrity and weight reduction. The study showcases successful outcomes, presenting quadcopter chassis designs that significantly improve structural efficiency and overall performance metrics. The findings contribute to aerial robotics and hold promise for precision agriculture applications with relevant performed simulations, emphasizing the importance of tailored design methodologies for other engineering domains. In conclusion, this research provides a foundational step toward advancing drone technology, with weight reductions of almost 50%, P/W and T/W ratios increment of 6.08% and 6.75%, respectively, at least an 11.8% increment in Factor of Safety, at least a 70% reduction in stress values and reduced manufacturing time from its comparative DJI F450 drone, demonstrating the critical role of innovative design approaches in optimizing operational efficiency for targeted applications.
]]>Machines doi: 10.3390/machines12030186
Authors: Xiaoyang Zhou
With the increasing demand for processing precision in the manufacturing industry, feed-rate scheduling is a crucial component in achieving the processing quality of complex surfaces. A smooth feed-rate profile not only guarantees machining quality but also improves machining efficiency. Although the typical offline feed-rate scheduling method possesses good processing efficiency, it may not provide an optimal solution due to the NP-hard problem caused by the feed-rate scheduling of continuous curve segments, which easily results in excess kinetic limitations and feed-rate fluctuations in a real-time interpolation. Instead, the FIR (Finite Impulse Response) method is widely used to realize interpolation in real-time processing. However, the FIR method will filter out a large number of high-frequency signals, leading to a low-processing efficiency. Further, greater acceleration or deceleration is required to ensure the interpolation passes through the segment end at a predefined feed rate and the deceleration in the feed rate profile appears earlier, which allows the interpolation to easily exceed the kinetic limitation. At present, a simple offline or online method cannot realize the global optimization of the feed-rate profile and guarantee the machining efficiency. Moreover, the current feed-rate scheduling that considers both offline and online methods does not consider the situation that the call of offline data and online prediction data will lead to a decrease in the real-time performance of the CNC system. Further, real-time feed-rate scheduling data tend to dominate the whole interpolation process, thus reducing the effect of the offline feed-rate scheduling data. Hence, based on the tool path with C3 continuity (Cubic Continuously Differentiable), this paper first presents a basic interpolation unit relevant to the S-type interpolation feed-rate profile. Then, an offline local smooth strategy is proposed to smooth the feed-rate profile and reduce the exceeding of kinetic limitations and feed-rate fluctuations caused by frequent acceleration and deceleration. Further, a global online smoothing strategy based on the data generated by offline pre-interpolation is presented. What is more, FIR login and logout conditions are proposed to further smooth the feed-rate profile and improve the real-time performance and machining efficiency. The case study validates that the proposed method performs better in kinetic results compared with the typical offline and FIR methods in both the simulation experiment and actual machining experiments. Especially, in actual processing experiments, the proposed method obtains a 28% reduction in contour errors. Further, the proposed method compared with the FIR method obtains a 15% increase in machining efficiency but only a 4% decrease compared with the typical offline method.
]]>Machines doi: 10.3390/machines12030185
Authors: Xiaohui Liu Haofeng Liu Hui Qiao Sihan Zhou Liang Qin
This paper focus on direct current (DC) filter grounding faults to propose a novel dilated normalized residual convolutional neural network (DRNCNN) fault diagnosis model for high-voltage direct current (HVDC) transmission systems. To address the insufficiency of the traditional model’s receptive field in dealing with high-dimensional and nonlinear data, this paper incorporates dilated convolution and batch normalization (BN), significantly enhancing the CNN’s capability to capture complex spatial features. Furthermore, this paper integrates residual connections and parameter rectified linear units (PReLU) to optimize gradient propagation and mitigate the issue of gradient vanishing during training. These innovative improvements, embodied in the DRNCNN model, substantially increase the accuracy of fault detection, achieving a diagnostic accuracy rate of 99.28%.
]]>Machines doi: 10.3390/machines12030184
Authors: Xing Shui Zhijun Rong Binbin Dan Qiangjian He Xin Yang
Complex, thin-walled components are the most important load-bearing structures in aircraft equipment. Monitoring the wear status of milling cutters is critical for enhancing the precision and efficiency of thin-walled item machining. The cutting force signals of milling cutters are non-stationary and non-linear, making it difficult to detect wear stages. In response to this issue, a system for monitoring milling cutter wear has been presented, which is based on parameterized Variational Mode Decomposition (VMD) Multiscale Permutation Entropy. Initially, an updated whale optimization technique is used, with the joint correlation coefficient serving as the fitness value for determining the VMD parameters. The improved VMD technique is then used to break down the original signal into a series of intrinsic mode functions, and the Multiscale Permutation Entropy of each effective mode is determined to generate a feature vector. Finally, a 1D Convolutional Neural Network (1D CNN) is employed as the input model for state monitoring using the feature vector. The experimental findings show that the suggested technique can efficiently extract characteristics indicating the wear condition of milling cutters, allowing for the precise monitoring of milling cutter wear states. The recognition rate is as high as 98.4375%, which is superior to those of comparable approaches.
]]>Machines doi: 10.3390/machines12030183
Authors: Luca Landi Giulia Morettini Massimiliano Palmieri Stefano Benicchi Filippo Cianetti Claudio Braccesi
In recent years, polymeric materials have gained prominence as a competitive option for gear manufacturing. Nevertheless, the absence of comprehensive literature addressing the wear due to the coupling of these materials presents a real challenge in response to this innovative trend. Wear of plastic gearwheels represents, in fact, a key issue, traditionally assessed using standard formulations under optimal dry operating conditions. These calculations often rely on coefficients derived from specialized gear tests, but their applicability is constrained to specific polymer–metal combinations. This research was dedicated to the development of a test bench tailored to evaluate the wear of glass fiber-reinforced self-lubricating polymer gearwheels under different operating conditions. This study commenced with a comprehensive exploration of wear phenomena in thermoplastic gearwheels and the inherent challenges associated with utilizing existing standards and the scientific literature for wear analysis. This was followed by a careful evaluation of the operational needs of the test bench, which, starting from a basic solution already implemented, improved its use in various aspects. Finally, this study introduced an optical-based methodology for average linear wear control. This research strived to establish a testing approach that minimizes uncertainties when assessing the wear of thermoplastic gears.
]]>Machines doi: 10.3390/machines12030182
Authors: Sean Mather Arthur Erdman
Gears are foundational tools used to transmit or modify mechanical energy or motion. Implementing gears into planar linkage mechanisms is less common but can be a similarly valuable technique that takes advantage of the high efficiency of gears while producing complex and precise motions. While recent numerical methods for designing these geared planar linkage mechanisms (GPLMs) have proliferated in the literature, analytical approaches have their merits and have received less attention. Here, an analytical alternative is presented as a modification of the complex-number loop-based synthesis method for designing multiloop mechanisms. Some of the base topologies for geared dyad, triad, and quadriad chains are presented, along with a numerical example demonstrating the solution procedure’s effectiveness.
]]>Machines doi: 10.3390/machines12030181
Authors: Carsten Knoll Julius Fiedler Stefan Ecklebe
In this paper, we introduce a novel method to formally represent elements of control engineering knowledge in a suitable data structure. To this end, we first briefly review existing representation methods (RDF, OWL, Wikidata, ORKG). Based on this, we introduce our own approach: The Python-based imperative representation of knowledge (PyIRK) and its application to formulate the Ontology of Control Systems Engineering (OCSE). One of its main features is the possibility to represent the actual content of definitions and theorems as nodes and edges of a knowledge graph, which is demonstrated by selected theorems from Lyapunov’s theory. While the approach is still experimental, the current result already allows the application of methods of automated quality assurance and a SPARQL-based semantic search mechanism. The feature set of the framework is demonstrated by various examples. The paper concludes with a discussion of the limitations and directions for further development.
]]>Machines doi: 10.3390/machines12030180
Authors: Cosmin Constantin Grigoras Valentin Zichil Vlad Andrei Ciubotariu Stefan Marius Cosa
This review focuses on the complex connections between machine learning, mechatronics, and stretch forming, offering valuable insights that can lay the groundwork for future research. It provides an overview of the origins and fundamentals of these fields, emphasizes notable progress, and explores the influence of these fields on society and industry. Also highlighted is the progress of robotics research and particularities in the field of sheet metal forming and its various applications. This review paper focuses on presenting the latest technological advancements and the integrations of these fields from their beginnings to the present days, providing insights into future research directions.
]]>Machines doi: 10.3390/machines12030179
Authors: Jin Yan Jianbin Liao Weiwei Zhang Jinliang Dai Chaoming Huang Hanlin Li Hongliang Yu
In this paper, a graph convolutional network is constructed and applied for bearing fault diagnosis. Specifically, the constant-Q transform (CQT) is first adopted for spectral analysis of vibration signals, where the frequencies are distributed in the logarithmic scale. Varied frequency resolutions can be obtained to satisfy the spectral resolution requirement and reduce signal dimension. Afterwards, the CQT spectrum is modeled by a graph, where nodes are frequency bins and edges reflect the inner relationship of different bins. There are edges between the fundamental and harmonic components. Then, a two-layer graph convolutional network (GCN) is utilized to assess the significance of vibration sources within the mixed signals. Finally, the bearing faults are determined according to the output of the GCN. To the best of our knowledge, this is the first work to model the vibration signal in this graph structure. The advantage of this approach lies in the simplification of edge definitions, facilitating shared connectivity relationships between the fundamental frequency and harmonics. Its performance was compared with another state-of-the-art fault diagnosis model. Experimental results demonstrate that the proposed model obtains higher accuracy, and it is more effective in extracting discriminative features.
]]>Machines doi: 10.3390/machines12030178
Authors: Magno Ayala Jesus Doval-Gandoy Jorge Rodas Osvaldo Gonzalez Raúl Gregor Larizza Delorme Carlos Romero Ariel Fleitas
The predictive current controller has arisen as a practicable technique for operating multiphase machines due to its fast dynamic response, control flexibility, and overall good performance. However, this type of controller has limitations, e.g., it tends to suffer from steady-state tracking errors in (d−q) currents; high computational burden; and high (x−y) currents, which become more pronounced at higher speeds, thereby worsening its sustainability. While some proposals have addressed these limitations by incorporating modulation stages and new cost functions, there is still room for improvement, particularly at higher speeds. In line with the pursuit of sustainable advancements, this article explores the integration of a field-weakening strategy with a modulated predictive current controller applied to a six-phase induction machine to improve its performance at current tracking for higher speed ranges. Experimental tests were conducted to validate the effectiveness of the proposed controller, assessing stator current tracking, reduction in the (x−y) currents, and the total harmonic distortion.
]]>Machines doi: 10.3390/machines12030177
Authors: Xiaoning Song Kaifu Mi Yu Lei Zhengyang Li Dongjia Yan
Erosion of solid particles in a pipe elbow containing a 90° angle is investigated by simulation methods. In the process of shale gas exploitation, the impact of solid particles carried by fluid on the inner surface wall of pipes, as well as the turbulent flow, cause the erosion of pipes, which brings about heavy economic losses for the oil and gas industry. In the impact erosion of the inner surface wall of the pipe, the worst erosion occurs at the elbow. In this study, the erosion of a pipe elbow which has been widely used in actual production is analyzed, and the influence of the fluid velocity, the solid particle size, and the wall roughness on the erosion is investigated. Additionally, the simulation results of the erosion with the rebound and freeze boundary conditions are compared, indicating that setting the freeze boundary condition could significantly improve the computational efficiency by 74% with the acceptable accuracy. In order to reduce the impact erosion in the pipe elbow containing a 90° angle, an optimal design is proposed that can reduce the maximum erosion rate by 52.4%. These results complement the research of elbow erosion and provide ideas for the optimization problem of a pipe elbow containing a 90° angle.
]]>Machines doi: 10.3390/machines12030176
Authors: Zhiqiang He Fugang Zhai Changyu Tan Xiaojun Chen Tianshuo Chen Pengpeng Ma
With the increasing demand for lightweight construction machinery, it is of great significance to study non-metallic materials that can replace steel plates to make hydraulic oil tanks (HOTs). To explore the feasibility of making HOTs with three materials—cross-linked polyethylene (XLPE), polypropylene (PP), and nylon (PA)—this paper takes 28 L and 115 L volumes commonly used in construction machinery, such as forklifts and loaders, as the design volume and obtains non-metal HOT products of good forming quality by regulating the process parameters. Based on the test methods and evaluation bases of the fuel tank in the national standard, the normal-temperature pressure test, high-temperature pressure test, and low-temperature impact test are designed according to the working conditions of the HOTs. Finally, the non-metallic HOT products are tested. The results show that the rotational molding of XLPE material is the easiest, and products of all sizes can be molded, but the mechanical properties and thermal stability of the products are poor. The low-temperature impact resistance of PP products is poor. PA material can be used to create small HOTs, and the product performance is excellent. This research serves as a valuable reference for the non-metallic and lightweight design of HOTs.
]]>Machines doi: 10.3390/machines12030175
Authors: Yi-Fan Cui Ying-Hui Zhang Wei-Dong He Lian-Jun Dong
Focusing on the investigation of a 3 MW wind-turbine gearbox, this paper’s aim is to address the challenge of turbine shutdown due to the internal oil temperature exceeding its limits. Additionally, there is the difficulty in measuring the internal temperature. To tackle these issues, a thermal network model for the entire gearbox was developed. This model is based on an analysis of the thermodynamic behavior of the three-stage transmission in the wind-turbine gearbox and internal oil-spray lubrication. Through this model, thermal balance equations were established to predict the steady-state temperatures under different operating conditions. This study delved into the calculation methods for the power loss of heat sources in thermodynamic balance equations and the calculation methods for different types of thermal resistance between nodes, forming an adapted computational process. Applying this model, simulated analyses yielded temperatures at various nodes and bearing temperatures under different operating conditions. These results were compared with actual SCADA data, and steady-state thermal simulations of the high-speed stages were conducted, demonstrating the model’s effectiveness in predicting steady-state temperatures for a large-megawatt wind-turbine gearbox. Furthermore, the model-based analysis explored the impact of the oil spray parameters on the gearbox temperature, providing a theoretical foundation for further anticipating overheating malfunctions and optimizing the internal cooling systems.
]]>Machines doi: 10.3390/machines12030174
Authors: Tatsuhiko Aizawa Tomohiro Miyata Kiyoyuki Endoh
The two-step PM (powder metallurgy)-route procedure was proposed to fabricate a super-engineering plastic gear directly from powder feedstock. Its lightweight, fully dense integrity and high-stiffness has been found to be suitable for reducers in robotics and electric vehicles, as they work even in severe environmental conditions. In this study, the green compaction and sinter-forging processes were used to consolidate the polyimide powder feedstock and to sinter forge the solid preform into the final products. To demonstrate the high density of preforms and sinter-forged gears, a hardness measurement and X-ray computer tomography were employed. The gear-grade balancing was also evaluated to describe the effect of fine sinter-forging conditions on the dimensional quality of polyimide gears. High gear grade with JIS-2 class proved that the polyimide was useful as a matrix of lightweight and high-strength gears.
]]>Machines doi: 10.3390/machines12030173
Authors: Binrui Zhang Min Ye Gaoqi Lian Yan Li Baozhou Xia
The comprehensive performance of unmanned excavators is crucial for the development and optimization of the field of construction machinery. To effectively improve the unmanned excavator to meet the needs of the market, it is imperative to quantify the evaluation method of the comprehensive performance of unmanned excavators. In this study, an evaluation method combining a fuzzy analytic hierarchy process and multivariate image area analysis method is proposed. Firstly, based on the feature extraction of the signal stability of the unmanned excavators, fifteen evaluation indexes were proposed. Then, the case study is used to obtain the scores corresponding to these indexes. The fuzzy analytic hierarchy process is applied to determine the relative weight of the selected evaluation criteria, in which the uncertain and imprecise judgments of decision makers are converted into fuzzy numbers. At the same time, the braking performance of the three types of unmanned excavators was comprehensively evaluated and ranked using the multivariate image area analysis method as an empirical example. Finally, a weight analysis is performed to check the robustness of the ranking results. The results show that the proposed method is effective and feasible. It provides a reference for the performance improvement and efficiency optimization of unmanned excavators.
]]>Machines doi: 10.3390/machines12030172
Authors: Larisa Rybak Giuseppe Carbone Dmitry Malyshev Artem Voloshkin
Aliquoting of biological samples refers to the process of dividing a larger biological sample into smaller, representative portions known as aliquots. This procedure is commonly employed in laboratories, especially in fields like molecular biology, genetics, and clinical research. Currently, manual dosing devices are commonplace in laboratories, but they demand a significant amount of time for their manual operation. The automated dosing devices available are integrated into narrowly focused aliquoting systems and lack versatility as manipulator equipment. Addressing this limitation, a novel technical solution is proposed in this paper for a modular dosing device compatible with robotic manipulators. The paper introduces and details a mathematical model, optimizes its parameters, and constructs a detailed 3D model using the NX environment to demonstrate the engineering feasibility of our concept. It further outlines the development of a three-dimensional dynamic simulation model for the dosing device, comparing analytical calculations with simulation results. The construction of a dosing device prototype is discussed, followed by a comprehensive experimental validation.
]]>Machines doi: 10.3390/machines12030171
Authors: Anton Hoyer Eckart Uhlmann
Brushing with bonded abrasives is a finishing process used for deburring, edge rounding, and roughness reduction. However, due to the complex motion, chipping, and wear behavior of abrasive filaments, industrial brushing processes have historically relied on empirical knowledge. To gain a better understanding of filament interactions, a physical model based on the discrete element method was developed to simulate process forces and contact areas. Filament patterns of round brushes were determined through the use of laser line triangulation and image processing. These filament patterns showed interlocked filaments and yielded more accurate results when used in brushing simulations than the oversimplified square patterns, which were used in previous research. Simulation confirms the occurrence of filament interactions, distinguishes between sweeping and striking filament motions, and reveals dynamic behavior at high brushing velocities that may increase undesirable tool wear.
]]>Machines doi: 10.3390/machines12030170
Authors: Jiahao Wang Zhengqing Liu Yang Wu Qiucheng Wang Dayu Shu
Tantalum–tungsten alloys have been widely used in different industrial sectors—for example, in chemical, medical, aerospace, and military equipment. However, they are usually difficult to cut because of the large cutting force, rapid tool wear, and poor surface finish during machining. This paper presents the machining performance and cutting tool wear of AlCrN/TiAlN-coated carbide tools during the milling process of Ta-2.5W. The effects of cutting parameters on the cutting forces and surface roughness of AlCrN/TiAlN-coated carbide tools were obtained and analyzed. The results show that the wear resistance of AlCrN-coated tools is better than that of TiAlN-coated tools, and that the main wear mechanisms of both cutting tools are crater wear, adhesive wear, and diffusion wear. Compared to TiAlN-coated tools, AlCrN-coated tools reduced the cutting forces by 1% to 15% and decreased the surface roughness by 6% to 20%. A cutting speed within the range of 80–120 m/min can ensure a low cutting force while maintaining good surface roughness, which is more conducive to machining Ta-2.5W.
]]>Machines doi: 10.3390/machines12030169
Authors: Wang-Su Jeon Sang-Yong Rhee
The advancement of smart factories has brought about small quantity batch production. In multi-variety production, both materials and processing methods change constantly, resulting in irregular changes in the progression of tool wear, which is often affected by processing methods. This leads to changes in the timing of tool replacement, and failure to correctly determine this timing may result in substantial damage and financial loss. In this study, we sought to address the issue of incorrect timing for tool replacement by using a Seq2Seq model to predict tool wear. We also trained LSTM and GRU models to compare performance by using R2, mean absolute error (MAE), and mean squared error (MSE). The Seq2Seq model outperformed LSTM and GRU with an R2 of approximately 0.03~0.037 in step drill data, 0.540.57 in top metal data, and 0.16~0.45 in low metal data. Confirming that Seq2Seq exhibited the best performance, we established a real-time monitoring system to verify the prediction results obtained using the Seq2Seq model. It is anticipated that this monitoring system will help prevent accidents in advance.
]]>Machines doi: 10.3390/machines12030168
Authors: Nikolaos E. Karkalos Panagiotis Karmiris-Obratański
Non-conventional processes are considerably important for the machining of hard-to-cut alloys in various demanding applications. Given that the surface quality and integrity, dimensional accuracy, and productivity are important considerations in industrial practice, the prediction of the outcome of the material removal process should be able to be conducted with sufficient accuracy, taking into consideration the computational cost and difficulty of implementation of the relevant models. In the case of AWJ, various types of approaches have been already proposed, both relying on analytical or empirical models and developed by solving partial differential equations. As the creation of a model for AWJ pocket milling is rather demanding, given the number of parameters involved, in the present work, it is intended to compare the use of three different types of efficient modeling approaches for the prediction of the dimensions of pockets milled by AWJ technology. The models are developed and evaluated based on experimental results of AWJ pocket milling of a titanium workpiece by an eco-friendly walnut shell abrasive. The results indicate that a semi-empirical approach performs better than a two-step hybrid analytical/semi-empirical method regarding the selected cases, but both methods show promising results regarding the realistic representation of the pocket shape, which can be further improved by a probabilistic approach.
]]>Machines doi: 10.3390/machines12030167
Authors: Hoang-Long Dang Sangshin Kwak Seungdeog Choi
DC microgrids are vital for integrating renewable energy sources into the grid, but they face the threat of DC arc faults, which can lead to malfunctions and fire hazards. Therefore, ensuring the secure and efficient operation of DC systems necessitates a comprehensive understanding of the characteristics of DC arc faults and the implementation of a reliable arc fault detection technique. Existing arc-fault detection methods often rely on time–frequency domain features and machine learning algorithms. In this study, we propose an advanced detection technique that utilizes a novel approach based on feature differences between moving intervals and advanced learning techniques (ALTs). The proposed method employs a unique approach by utilizing a time signal derived from power supply-side signals as a reference input. To operationalize the proposed method, a meticulous feature extraction process is employed on each dataset. Notably, the difference between features within distinct moving intervals is calculated, forming a set of differentials that encapsulate critical information about the evolving arc-fault conditions. These differentials are then channeled as inputs for advanced learning techniques, enhancing the model’s ability to discern intricate patterns indicative of DC arc faults. The results demonstrate the effectiveness and consistency of our approach across various scenarios, validating its potential to improve fault detection in DC systems.
]]>Machines doi: 10.3390/machines12030166
Authors: Jing Zhou Haili Li Lin Lu Ying Cheng
A set of online inspection systems for surface defects based on machine vision was designed in response to the issue that extrusion molding ceramic 3D printing is prone to pits, bubbles, bulges, and other defects during the printing process that affect the mechanical properties of the printed products. The inspection system automatically identifies and locates defects in the printing process by inspecting the upper surface of the printing blank, and then feeds back to the control system to produce a layer of adjustment or stop the printing. Due to the conflict between the position of the camera and the extrusion head of the printer, the camera is placed at an angle, and the method of identifying the points and fitting the function to the data was used to correct the camera for aberrations. The region to be detected is extracted using the Otsu method (OSTU) on the acquired image, and the defects are detected using methods such as the Canny algorithm and Fast Fourier Transform, and the three defects are distinguished using the double threshold method. The experimental results show that the new aberration correction method can effectively minimize the effect of near-large selection caused by the tilted placement of the camera, and the accuracy of this system in detecting surface defects reached more than 97.2%, with a detection accuracy of 0.051 mm, which can meet the detection requirements. Using the weighting function to distinguish between its features and defects, and using the confusion matrix with the recall rate and precision as the evaluation indexes of this system, the results show that the detection system has accurate detection capability for the defects that occur during the printing process.
]]>Machines doi: 10.3390/machines12030165
Authors: Yong Hoon Jang Han Sol Kim
This study aims to propose a sampled-data control technique, utilizing a linear matrix inequality (LMI) approach, to achieve string-stable vehicle platooning in a cooperative adaptive cruise control (CACC) system with communication delays. To do this, a decentralized sampled-data controller design technique that combines one controller using sensor measurements and another one utilizing vehicle-to-vehicle (V2V) communication, ensuring both individual and string stability, is proposed first. Next, a memory sampled-data control (MSC) approach is presented to account for transmission delays in V2V communication. Additionally, an improved Lyapunov–Krasovskii functional (LKF) is presented to improve computational complexity and sampling performance. The design conditions are formulated as linear matrix inequalities (LMIs) in the time domain, facilitating efficient stability analysis and optimization. Finally, vehicle platooning simulations are provided to validate the effectiveness and feasibility of the proposed technique.
]]>Machines doi: 10.3390/machines12030164
Authors: Chungeng Sun Jipeng Li Ying Tan Zhijie Duan
High-precision tracking of an electro-hydraulic servo material testing machine’s force control system was achieved using a proposed integral sliding mode control method based on feedback linearization to improve the machine’s force control performance and anti-interference ability. First, the electro-hydraulic servo system’s nonlinear mathematical model was established, and its input–output linearization was realized using differential geometry theory. Second, integral sliding mode control was introduced into the controller and the feedback-linearized integral sliding mode controller was designed. The controller’s stability was proven based on the Lyapunov stability principle. Finally, a simulation model of the electro-hydraulic servo material testing machine’s force control system was established using AMESim/Simulink software. The designed controller was simulated and verified, and the control effects of the system’s different amplitudes and frequency signals were analyzed. The results showed that the feedback-linearized integral sliding mode control algorithm could effectively improve the system’s force tracking accuracy and parameter adaptability, yielding better robustness and a better control effect.
]]>Machines doi: 10.3390/machines12030163
Authors: Erick Axel Padilla-García Raúl Dalí Cruz-Morales Jaime González-Sierra David Tinoco-Varela María R. Lorenzo-Gerónimo
Although additive manufacturing is a relatively new technology, it has been widely accepted by industry and academia due to the wide variety of prototypes that can be built. Furthermore, using mobile robots to carry out different tasks allows greater flexibility than using manipulator robots. In that sense, and based on those above, this article focuses on the design and assembly of a multi-configurable mobile robot that is capable of changing from a differential to an omnidirectional configuration. For this purpose, a sequential mechatronic design/control methodology was implemented to obtain an affordable platform via additive manufacturing which is easily scalable and allows the user to change from one configuration to another. As a proof of concept, this change is made manually. Fabrication, construction, and assembly processes for both structures are presented. Then, a hierarchical control law is designed. In this sense and based on Lyapunov’s method, a low-level controller is developed to control the angular speed of the wheels to a desired angular speed, and a medium-level controller controls the robot’s attitude to follow a desired Cartesian trajectory. Finally, the control strategies are implemented in both prototype configurations, and through experimental results, the theoretical analysis and the construction of the mobile robot are validated.
]]>Machines doi: 10.3390/machines12030162
Authors: Chunjiang He Jinxu Zhang Chao Lin
An atypical face gear pair with complex transmission motion can be used in intermittent reciprocating mechanisms with more precise transmission and a much higher capacity than conventional mechanisms, such as cams and linkages. In this study, we derive a mathematical equation for the complex tooth surface of this gear pair. We indicate the change in root cutting, top sharpening and the effective width of the tooth surface with different parameters. Additionally, we derive the governing equation for the kinematical characteristics of this eccentric curve-face gear pair with a rigid–flexible coupling system, revealing the continuous intermittent contact principle of this gear type with different parameters. Boundary conditions for the gear pair are proposed, demonstrating that the vibration of the gear pair is more obvious, even at a low velocity. In addition, the critical velocity, which mostly ranges from 300 rpm to 400 rpm, is affected by the stiffness of the frames and the parameters of the tooth surfaces. The interval space and interval time of the intermittent contact system are Δd≤0.3 mm and Δt≤5.6×10−4 s, with visible surface sliding on the contact area. It is shown that the contact points are firstly concentrated at the outer part of the tooth surface and that the meshing will break off at the first tooth with the minimum inner radius RGi−min. These theoretical results, which have been verified experimentally, provide theoretical support for further analysis and the better application of this unconventional gear pair.
]]>Machines doi: 10.3390/machines12030161
Authors: Wu Wang Hua Li Pei Liu Botong Niu Jing Sun Boge Wen
Using optimal assembly relationships, companies can enhance product quality without significantly increasing production costs. However, predicting Assembly Geometric Errors presents a challenging real-world problem in the manufacturing domain. To address this challenge, this paper introduces a highly efficient Transformer-based neural network model known as Predicting Assembly Geometric Errors based on Transformer (PAGEformer). This model accurately captures long-range assembly relationships and predicts final assembly errors. The proposed model incorporates two unique features: firstly, an enhanced self-attention mechanism to more effectively handle long-range dependencies, and secondly, the generation of positional information regarding gaps and fillings to better capture assembly relationships. This paper collected actual assembly data for folding rudder blades for unmanned aerial vehicles and established a Mechanical Assembly Relationship Dataset (MARD) for a comparative study. To further illustrate PAGEformer performance, we conducted extensive testing on a large-scale dataset and performed ablation experiments. The experimental results demonstrated a 15.3% improvement in PAGEformer accuracy compared to ARIMA on the MARD. On the ETH, Weather, and ECL open datasets, PAGEformer accuracy increased by 15.17%, 17.17%, and 9.5%, respectively, compared to the mainstream neural network models.
]]>Machines doi: 10.3390/machines12030160
Authors: Michele Asperti Michele Vignati Edoardo Sabbioni
Torque vectoring is a widely known technique to improve vehicle handling and to increase stability in limit conditions. With the advent of electric vehicles, this is becoming a key topic since it is possible to have distributed powertrains, i.e., multiple motors are adopted, in which each motor is controlled separately from the others. Moreover, electric motors deliver the torque required by the controller faster and more precisely than internal combustion engines, active differentials and conventional hydraulic brakes. The state of the art of Direct Yaw Moment Control (DYC) techniques, ranging from classical to modern control theories, are analyzed and discussed in this paper. The aim is to give an overview of the currently available approaches while identifying their drawbacks regarding performances and robustness when dealing with common issues like model uncertainties, external disturbances, friction limit and common state estimation problems. This contribution analyzes all the steps from the lateral dynamics reference generation to the desired control action computation and allocation to the available actuators. In addition, some of the presented control logic is evaluated in a simulation environment for a passenger car. Results of both open-loop and closed-loop maneuvers allow the comparison and clarification of each control strategy’s key advantages.
]]>Machines doi: 10.3390/machines12030159
Authors: Bassam Hasanain
The study and implementation of ergonomics are vital for the growth of industries and improvement in work cultures. Sustainable manufacturing cannot be achieved without the implementation of human-factor ergonomics. Ergonomics is used to analyze the link between research studies and industrial practices in order to maximize the efficiency of processes by keeping in view the well-being of workforce. Designing tools, tasks, machines, systems, jobs, and settings for efficient, safe, and successful human usage involves applying knowledge about human behavior, abilities, and limitations. Workers are the backbone of the manufacturing economy. The review outlines significant advancements in preventing ergonomic problems during the design stage of the manufacturing process to achieve sustainability. The bibliometric analysis is used to identify the literature base for ergonomics. To maximize the benefits of ergonomics and to integrate sustainable practices, various methods are required to organize existing processes and technologies. The human-centered design identifies problems and aligns the output with the intended objectives of sustainability. The goal of human factors and ergonomics is to successfully integrate people into systems and develop the manufacturing processes around the well-being of workers and sustainability principles. Similarly, ergoecology, eco-ergonomics, and green ergonomics are frequently used for sustainable manufacturing. Achieving sustainability in manufacturing is not possible without considering human ergonomics. Ergonomists frequently research management, planning, and other topics to increase the efficiency of the manufacturing process. Efficient worker performance and quality of life can be enhanced through work design, management, and organizational ergonomics. Human ergonomics relates sustainability with cognitive variables such as situational awareness, human reliability, and decision-making abilities. This review explains the role of human factors and ergonomics for sustainable manufacturing.
]]>Machines doi: 10.3390/machines12030158
Authors: Ashish Kumar Sahu Reemon Z. Haddad Dhafar Al-Ani Berker Bilgin
Interior permanent magnet synchronous motors (IPMSMs) are extensively used as traction motors today because of their exceptional torque, power density, and wide, constant power operating range. Under real-world usage, an IPMSM rotor undergoes varying electromagnetic, thermal, and mechanical loads. Under such conditions, fatigue life-based design criteria should be used over stress-based design criteria to ensure the structural integrity of the rotor. Moreover, the driving dynamics can change the rotor temperature continuously, which affects the electromagnetic, mechanical, and fatigue properties of the rotor material. This paper proposes a robust thermomechanical rotor fatigue simulation workflow considering significant loads acting on an IPMSM rotor and the temperature variation throughout a drive cycle. It discusses an accelerated fatigue life estimation approach based on the peak valley extraction method to reduce the simulation time significantly for the stress and fatigue analysis. Then, it presents a method for a stress-life curve generation for variable loading. It also presents a sensitivity study with a median S-N curve, and a 90% reliability and 95% confidence (R90C95) S-N curve.
]]>Machines doi: 10.3390/machines12030157
Authors: Nicolás Mendoza Mahdi Haghshenas-Jaryani
This paper presents the design, development, and testing of a robot that combines soft-body grasping and crawling locomotion to navigate tubular objects. Inspired by the natural snakes’ climbing locomotion of tubular objects, the soft robot includes proximal and distal modules with radial expansion/contraction for grasping around the objects and a longitudinal contractile–expandable driving module in-between for providing a bi-directional crawling movement along the length of the object. The robot’s grasping modules are made of fabrics, and the crawling module is made of an extensible pneumatic soft actuator (ePSA). Conceptual designs and CAD models of the robot parts, textile-based inflatable structures, and pneumatic driving mechanisms were developed. The mechanical parts were fabricated using advanced and conventional manufacturing techniques. An Arduino-based electro-pneumatic control board was developed for generating cyclic patterns of grasping and locomotion. Different reinforcing patterns and materials characterize the locomotor actuators’ dynamical responses to the varying input pressures. The robot was tested in a laboratory setting to navigate a cable, and the collected data were used to modify the designs and control software and hardware. The capability of the soft robot for navigating cables in vertical, horizontal, and curved path scenarios was successfully demonstrated. Compared to the initial design, the forward speed is improved three-fold.
]]>Machines doi: 10.3390/machines12030156
Authors: Nikolaos A. Fountas Ioannis Papantoniou Dimitrios E. Manolakos Nikolaos M. Vaxevanidis
Advances in machining technology and materials science impose the identification of optimal settings for process-related parameters to maintain high quality and process efficiency. Given the available resources, manufacturers should determine an advantageous process parameter range for their settings. In this work, the machinability of a special tool steel (UNIMAX® by Uddeholm, Sweden) under dry CNC turning is investigated. The working material is examined under two states; annealed and hardened. As major machinability indicators, main cutting force Fz (N) and mean surface roughness Ra (μm) were selected and studied under different values for the cutting conditions of cutting speed, feed rate, and depth of cut. A systematic experimental design was established as per the response surface methodology (RSM). The experimental design involved twenty base runs with eight cube points, four center points in the cube, six axial points, and two center points in the axial direction. Corresponding statistical analysis was based on analysis of variance and normal probability plots for residuals. Two regression models referring to main cutting force and surface roughness for both the annealed and hardened states of the material were developed and used as objective functions for subsequent evaluations by three modern meta-heuristics under the goal of machinability optimization, namely multi-objective grey wolf algorithm, multi-objective multi-verse algorithm and multi-objective ant lion algorithm. All algorithms were found capable of providing beneficial Pareto-optimal solutions for both main cutting force and surface roughness simultaneously whilst regression models achieved high correlation among input variables and optimization responses.
]]>Machines doi: 10.3390/machines12030155
Authors: Yongqi Xia Shibo Deng Mingtao Wu Binkun Ni
The coarse-grained electroplated diamond grinding wheels is increasingly favored in precision grinding of hard and brittle materials owing to its high material removal efficiency, high wear resistance and steady surface contour accuracy. However, how to determine whether the dressed grinding wheel surface topography can achieve the desired precision ground surface quality is still a huge challenge to this day. In this paper, a novel numerical simulation model, which was established basing on the statistical features of actual electroplated coarse-grained diamond grinding wheel and the kinetics of the grinding process, was proposed for theoretically and thoroughly studying the influence of the surface dressing depth of coarse-grained electroplated diamond grinding wheel on ground workpiece surface morphology. At first, the statistical features of actual electroplated coarse-grained diamond grinding wheel was acquired and a novel numerical grinding wheel surface model was established. Subsequently, a numerical ground workpiece surface simulation model was also developed. And then, the evolving mechanism of the grinding wheel surface topography with the dressed wheel surface abrasive grain protrusion height was theoretically studied by numerical simulation. Moreover, the influence of the wheel surface abrasive grain protrusion height on the ground surface roughness was thoroughly researched by means of theoretical model and experiments. The simulation and experiments results in this paper indicated that precision ground workpiece surface with nano-scale surface roughness can be acquired by grinding with a dressed grinding wheel with a certain abrasive grain protrusion height of 25% of the typical abrasive size. Comparing with the undressed grinding wheel (grinding wheel with original surface topography and not be dressed), the surface roughness Sa and Sq of the surface ground with a well-dressed wheel can achieving a significant decrease of 97.75–99.77% and 97.57–99.73%, respectively. Therefore, carefully dressing the electroplated coarse-grained diamond grinding wheel is of great significance for obtaining a precision ground workpiece surface quality.
]]>Machines doi: 10.3390/machines12030154
Authors: Zian Wu Wenxian Yang Xiaoping Song Kexiang Wei
Pitch bearings in wind turbines are crucial components that enable safe blade pitching, optimize electrical power output, and ensure turbine protection. Traditional vibration analysis-based methods used for high-speed bearings are not applicable to monitoring pitch bearings, due to its slow non-integer cycle rotation. To address this issue, a stress-based pitch bearing monitoring method is proposed in this paper. First, finite element analysis is conducted to establish the relationship between the maximum surface stress on the outer race of the pitch bearing and the presence of cracks. This relationship allows the identification of cracks on the outer race and an assessment of their severity based on the value of the maximum surface stress. Second, the outer race of the pitch bearing is divided into several segments, and a singularity detection technique is employed to locate the position of cracks on the outer race based on the stresses measured from the segments. To verify the proposed method, a wind turbine pitch bearing test rig was developed in a laboratory. Experimental results have shown that the proposed method can effectively and accurately identify and locate cracks on the outer race of the bearing, thereby demonstrating its great potential as a reliable approach for monitoring the condition of wind turbine pitch bearings.
]]>Machines doi: 10.3390/machines12030153
Authors: Alexander Bott Simon Anderlik Robin Ströbel Jürgen Fleischer Andreas Worthmann
This study addresses the challenge of the optimization of milling in industrial production, focusing on developing and applying a novel framework for optimising manufacturing processes. Recognising a gap in current methods, the research primarily targets the underutilisation of advanced data analysis and machine learning techniques in industrial settings. The proposed framework integrates these technologies to refine machining parameters more effectively than conventional approaches. The research method involved the development of the framework for the realisation and analysis of measurement data from milling machines, focusing on six machine parts and employing a machine learning system for optimization and evaluation. The developed and realised framework in the form of a software demonstrator showed its applicability in different experiments. This research enables easy deployment of data-driven techniques for sustainable industrial practices, highlighting the potential of this framework for transforming manufacturing processes.
]]>Machines doi: 10.3390/machines12030152
Authors: Hao Lin Haipeng Geng Ling Li Leiming Song Xiaojun Hu
High-speed direct-drive permanent magnet synchronous motors (PMSMs), supported by elastic foil gas bearings, have broad applications, such as in microcompressors. However, some problems remain to be solved for the electrical performance analysis of PMSMs. For example, there is presently no related analytical model that can be used in rotor dynamics expression for this type of PMSM. This study aimed to establish theoretical models for electromagnetic force density and torque. The process involved both theoretical and experimental research. The analytic models of air gap magnetic density, electromagnetic force density, and electromagnetic performance were established for a PMSM with a parallel magnetized cylindrical permanent magnet. The analytic calculation was conducted, and the results of the analytic model were obtained. The analytical model of the electromagnetic torque and force can be applied in theoretical research on rotor dynamics. The model provides a theoretical basis and method for studying the influence of the electromagnetic load on rotor dynamics. A finite element simulation analysis of the electrical performance of the PMSM was carried out. An electrical performance experiment was conducted. The deviation between the experimental result and the theoretical value was less than 4%. This result indicated that the analytic models could be used in a dynamics analysis of compressors that are directly driven by a PMSM for application in engineering and industrial contexts.
]]>Machines doi: 10.3390/machines12030151
Authors: Sadaf Zeeshan Tauseef Aized Fahid Riaz
Using modern machines like robots comes with its set of challenges when encountered with unstructured scenarios like occlusion, shadows, poor illumination, and other environmental factors. Hence, it is essential to consider these factors while designing harvesting robots. Fruit harvesting robots are modern automatic machines that have the ability to improve productivity and replace labor for repetitive and laborious harvesting tasks. Therefore, the aim of this paper is to design an improved orange-harvesting robot for a real-time unstructured environment of orchards, mainly focusing on improved efficiency in occlusion and varying illumination. The article distinguishes itself with not only an efficient structural design but also the use of an enhanced convolutional neural network, methodologically designed and fine-tuned on a dataset tailored for oranges integrated with position visual servoing control system. Enhanced motion planning uses an improved rapidly exploring random tree star algorithm that ensures the optimized path for every robot activity. Moreover, the proposed machine design is rigorously tested to validate the performance of the fruit harvesting robot. The unique aspect of this paper is the in-depth evaluation of robots to test five areas of performance that include not only the accurate detection of the fruit, time of fruit picking, and success rate of fruit picking, but also the damage rate of fruit picked as well as the consistency rate of the robot picking in varying illumination and occlusion. The results are then analyzed and compared with the performance of a previous design of fruit harvesting robot. The study ensures improved results in most aspects of the design for performance in an unstructured environment.
]]>Machines doi: 10.3390/machines12030150
Authors: Qiang Li Markus Heß
The third-body particle-involved sliding contact between two rough rubbers with wavy surfaces is experimentally studied. The experiment is designed to isolate the direct contact between the first bodies so that friction resistance is induced completely by the interactions between the third-body particle and the surfaces of the rubbers. In dry contact of a single particle, it is found that the particle exhibits pure rolling during the sliding of the first bodies, and the macroscopic friction resistance for overcoming sliding does not depend on the particle size, but it is significantly influenced by the initial position of the surface waviness relative to the particle’s position. The behavior of the particle under lubricated conditions exhibited significant differences. Due to the low local friction at the interface, the particle rapidly glided down to the valley of the waviness during compression. This abrupt motion of the particle resulted in it coming to rest in a stable position, awaiting a substantial force to push it forward. The friction resistance in the case with lubrication was found to be independent of the initial position of the waviness, and its value consistently remained at the maximum found in dry contact. Therefore, lubrication actually increases the macroscopic friction resistance. An approximate solution for the specific case of dry contact is proposed to understand the friction behavior.
]]>Machines doi: 10.3390/machines12030149
Authors: Yingbo Wang Fengyuan Zuo Shuai Zhang Zhen Zhao
This article proposes a progressive frequency domain-guided depth model with adaptive preprocessing to solve the problem of defect detection with weak features based on X-ray images. In distinct intuitive surface defect detection tasks, non-destructive testing of castings using X-rays presents more complex and weak defect features, leading to lower accuracy and insufficient robustness on the part of current casting defect detection methods. To address these challenges, the proposed method establishes four specialized mechanisms to improve model accuracy. First, an adaptive image contrast enhancement method is proposed to enhance the features of defects in casting images to promote subsequent feature extraction and prediction. Second, a subtle clue mining module based on frequency domain attention is proposed to fully extract the discriminative features of casting defects. Third, a feature refinement module based on progressive learning is proposed to achieve a balance between feature resolution and semantic information. Finally, a refined deep regression supervision mechanism is designed to improve defect detection accuracy under strict intersection-to-union ratio standards. We established extensive ablation studies using casting defect images in GDXray, conducted detailed comparative experiments with other methods, and performed experiments to analyze the robustness of the resulting models. Compared with other X-ray defect detection methods, our framework achieves an average +4.6 AP. Compared to the baseline, our proposed refined deep regression supervision mechanism results in an improvement of 5.3 AP.
]]>Machines doi: 10.3390/machines12030148
Authors: Jiman Kim Hyunsu Kim
With the recent conversion of internal combustion engines to electric vehicles, new noise issues have arisen, and among them, the noise generated by internal vehicle auxiliary systems is being considered. This study introduces an electronic filter designed with a motor model featuring vibration components, aiming to minimize the noise and vibrations generated by a Brushed DC (BDC) motor commonly employed in vehicle internal systems. It introduces a method to identify the connectors and internal parameters used in the motor for the matching of the model and experimental motor, and to measure and estimate these parameters. The model is separated and executed to ensure convergence, and it is validated by comparing the analysis results with the measured values. A filter is designed using the model to reduce current oscillations in the motor, confirming a subsequent reduction in noise and vibration. This research suggests the potential to attenuate noise and vibration in already produced motors by attaching only a filter without modifying the internal motor structure. Moreover, it is anticipated that a filter can be designed to predict and mitigate the noise and vibration components of the motor based on changes in load.
]]>Machines doi: 10.3390/machines12020147
Authors: Mattia Maltauro Roberto Meneghello Gianmaria Concheri
In tolerancing activities focusing on the allocation of geometrical tolerances, many critical issues originate from the non-optimal assignment of responsibilities among the organization units involved. This paper aims to depict relations between different tolerancing activities and relevant specifications, assigning them to the proper actor and, therefore, expanding the ISO 8015:2011 “responsibility principle”. A classification among tolerancing activities, specifications, and media is proposed; a horizontal hierarchical framework among functional, manufacturing, and verification specifications and a vertical hierarchical framework along the supply chain are discussed. Examples of both hierarchical structures are presented.
]]>Machines doi: 10.3390/machines12020146
Authors: Junwoo Kim Moustafa El-Gindy Zeinab El-Sayegh
In this research, an 8 × 8 scaled electric combat vehicle (SECV) is built. The scaled vehicle is evaluated in both experimental and simulated methods to analyze its performance. The scaled vehicle is developed to apply the Ackermann condition by implementing the individual steering and individual wheel speed control system at low speed. Individual eight-wheel rotational velocity control and individual eight-wheel steering angle control in real time are developed and installed on the remotely controlled scaled vehicle to meet a perfect Ackermann condition. Three different steering scenarios are developed and applied: a traditional steering scenario (first and second axle steering), fixed third axle steering scenario (first, second, and fourth axle steering), and all-wheel steering scenario. Stationary evaluation, turn radius evaluation, and double lane change evaluation are conducted to verify the application of the Ackermann condition. The differences between the experimental results and the simulated data are within an acceptable range. An important demonstration of this research is the novel validation of physical and simulated data in the application of the Ackermann condition for eight-wheel steering and velocity control for the three steering scenarios.
]]>Machines doi: 10.3390/machines12020145
Authors: Marco Ceccarelli Susana Sanz Vicente Díaz Matteo Russo
A new portable arm exercise device is presented as a laboratory prototype to assist arm movements in rehabilitation therapies and movement exercises. Unlike the devices currently used, a portable design is proposed, with easy assembly and operational characteristics that enable it to be used by users in the home and in a familiar environment. Sensors are also provided on the rotating crank to validate and monitor the efficiency of the arm exercise. A low-cost prototype is assembled using off-the-shelf components and 3D-printed parts. Design issues are discussed and elaborated on to build a prototype for future laboratory testing using fairly simple experimental methodology. Preliminary testing by one author shows good feasibility of the device. The findings from the experimental results can be summarized as effective smooth-monitored cyclic motion in the crank rotation with limited values for acceleration less than 1 g and for acting user forces less than 22 N. The values detected are significantly lower in the left hand, with the testing subject being right-handed and healthy, without injury to her upper limbs.
]]>Machines doi: 10.3390/machines12020144
Authors: Paula Bastida-Molina Yago Rivera César Berna-Escriche David Blanco Lucas Álvarez-Piñeiro
The recharging of electric vehicles will undoubtedly entail an increase in demand. Traditionally, efforts have been made to shift their recharging to off-peak hours of the consumption curve, where energy demand is lower, typically during nighttime hours. However, the introduction of photovoltaic solar energy presents a new scenario to consider when synchronizing generation and demand curves. High-generation surpluses are expected during the central day hours, due to the significant contribution of this generation; these surpluses could be utilized for electric vehicle recharging. Hence, these demand-side management analyses present important challenges for electricity systems and markets. This research explores this overdemand avenue and presents a method for determining the ideal recharge curve of the electric vehicle. Consequently, with this objective of maximizing photovoltaic generation to cover as much of the foreseeable demand for electric vehicles as possible in future scenarios of the electrification of the economy, the six fundamental electric vehicle charging profiles have been analyzed. A practical scenario for 2040 is projected for the Canary Islands, estimating the potential levels of demand-side management and associated coverage. The coverage ranges from less than 20% to over 40%, considering the absence of demand-side management measures and the maximum displacement achievable through such measures.
]]>Machines doi: 10.3390/machines12020143
Authors: Jihao Duan Zhuofan Wu Jianbo Ren Gaochen Zhang
Abrasive disc grinding is currently a key manufacturing process to achieve better accuracy and high-quality surfaces of TC17 components. Grinding force, which results from the friction and elastic–plastic deformation during the contact and interaction between the abrasive grains and the workpiece, is a critical parameter that represents the grinding accuracy and efficiency. In order to understand the influence factors of grinding force, the characteristics of the flexible abrasive disc grinding process were studied. Considering the contact state between the abrasive tool and the workpiece, the theoretical model of normal grinding force was established in detail, from macro- and micro-perspectives. By conducting single-factor and orthogonal grinding experiments of TC17 components, the influence of different process parameters on the normal grinding force was revealed. The normal grinding force prediction models of the abrasive disc grinding process were developed based on the Box–Behnken design (BBD) and particle swarm optimization–back propagation (PSO-BP) neural networks, respectively. The results showed that the normal grinding force was negatively correlated with the disc rotational speed, and positively correlated with the contact angle, grinding depth, and feed rate, and the interaction of the factor feed rate and grinding depth was the more influential factor. Both the BBD and PSO-BP force models had good reliability and accuracy, and the mean absolute error (MAE) and mean relative error (MRE) of the above two prediction models were 0.22 N and 0.16 N, and 13.3% and 10.9%, respectively.
]]>Machines doi: 10.3390/machines12020142
Authors: Aleksandra Müller Steffen Wurm Phil Willecke Oliver Petrovic Werner Herfs Christian Brecher
The Industry 4.0 research initiative strives to facilitate globally interconnected, flexible, and highly adaptable production systems. The use of skill-based control mechanisms such as OPC UA skills offers the prospect of a straightforward and flexible interchange, as well as the seamless integration of individual participants and processes through standardized interfaces. Furthermore, by enhancing these skills with evaluation parameters pertinent to the processes, such as CO2 equivalents or the duration of specific skill executions, a foundation is laid for creating a customizable and adaptable composition of processes based on specific production process needs. In this article, the OPC UA skill concept is expanded with process-relevant properties, and a structured procedure for the introduction of skill-based process control is presented. The developed concept was implemented and tested on an industrial use case of glass pane completion. The aim of this publication is to demonstrate the potential of skill-based process control that has an integrated assessment of skills.
]]>Machines doi: 10.3390/machines12020141
Authors: Marko Jamšek Gal Sajko Jurij Krpan Jan Babič
This paper focuses on the development of a novel climbing robot that is designed for autonomous maintenance of vertical gardens in urban environments. The robot, designed with a unique five-legged structure, is equipped with a range of electrical and mechanical components, enabling it to autonomously navigate and maintain a specially designed vertical garden wall facilitating interactive maintenance and growth monitoring. The motion planning and control of the robot were developed to ensure precise and adaptive movement across the vertical garden wall. Advanced algorithms were employed to manage the complex dynamics of the robot’s movements, optimizing its efficiency and effectiveness in navigating and maintaining the garden structure. The operation of the robot in maintaining the vertical garden was evaluated during a two-week trial where the robot successfully performed nearly 8000 leg movements, with only 0.6% requiring human intervention. This demonstrates a high level of autonomy and reliability. This study concludes that the pentapod robot demonstrates significant potential for automating the maintenance of vertical gardens, offering a promising tool for enhancing urban green spaces.
]]>Machines doi: 10.3390/machines12020140
Authors: Petrica Radu Carol Schnakovszky
Milling parts with low rigidity (thin-walled parts) are increasingly attracting the interest of the academic and industrial environment, due to the applicability of these components in industrial sectors of strategic interest at the international level in the aerospace industry, nuclear industry, defense industry, automotive industry, etc. Their low rigidity and constantly changing strength during machining lead on the one hand to instability of the cutting process and on the other hand to part deformation. Solving both types of problems (dynamic and static) must be preceded by prediction of cutting forces as accurately as possible, as they have a significant meaning for machining condition identification and process performance evaluation. Since there are plenty of papers dealing with this topic in the literature, the current research attempts to summarize the models used for prediction of force in milling of thin-walled parts and to identify which are the trends in addressing this issue from the perspective of intelligent production systems.
]]>Machines doi: 10.3390/machines12020139
Authors: Anna Mičietová Mária Čilliková Robert Čep Branislav Mičieta Juraj Uríček Miroslav Neslušan
This study is focused on analysing residual stresses (RSs) after turning high-tempered bearing steel through the use of the X-ray diffraction (XRD) technique. Phase transformations expressed in terms of the near-surface white layer (WL) and the corresponding microhardness profiles are correlated with the RSs as well as the depth of the RS profiles. Normal and shear components of RS and FWHM (full width at half maximum) of the diffraction peaks are analysed as a function of cutting insert flank wear as well as the cutting speed. It was found that the influence of tool wear prevails over cutting speed, RSs tend to shift into the compressive region with increasing tool flank wear, and the valuable shear components of RSs can be found in the near-surface region when the cutting inserts of lower flank wear are employed. The increasing flank wear also increases the depth in which the compressive RSs can be found. Furthermore, surface RSs are affected by the phase transformation process (formation of re-hardened WL) as well as the superimposing mechanical and thermal load.
]]>Machines doi: 10.3390/machines12020138
Authors: Mariusz Deja Angelos P. Markopoulos
Advances and Trends in Non-conventional, Abrasive and Precision Machining 2021 [...]
]]>Machines doi: 10.3390/machines12020137
Authors: Tarik Zarrouk Mohammed Nouari Jamal-Eddine Salhi Abdelkader Benbouaza
Nomex honeycomb composite (NHC) cores have seen significant growth in recent years, particularly in the aeronautics, aerospace, naval and automotive industries. This development presents significant challenges in terms of improving machining quality, requiring the use of specialized cutting tools and favorable cutting techniques. In this context, experimental studies have been carried out to highlight the characteristics of the milling of NHCs by rotary ultrasonic machining (RUM). However, the rapid motion of the cutting tool and the inaccessibility of the tool/part interface prevent the visualization of the chip formation process. For this purpose, a three-dimensional numerical model for milling the NHC structure using RUM technology was developed by Abaqus Explicit software. On the basis of this model, the components of the cutting force, the quality of the machined surface and the chip accumulation in front of the cutting tool were analyzed. The numerical results agree with the experimental tests, demonstrating that the use of RUM technology effectively reduces the cutting force components. An in-depth analysis of the influence of feed component Fy on the quality of the generated surface was carried out, revealing that the surface quality improved with low values of feed component Fy. Furthermore, the impact of ultrasonic vibrations on the accumulation of chips in front of the cutting tool is particularly optimized, in particular for large amplitudes.
]]>Machines doi: 10.3390/machines12020135
Authors: Luca Vecchiato Matteo Negri Giulio Picci Luca Viale Giulio Zaltron Stefano Giacometti Giovanni Meneghetti
The optimization of the brake systems is crucial for vehicle performance and safety of Formula SAE (FSAE) race cars. This study introduces a specialized brake test bench designed to enhance the understanding and testing of these systems. The bench integrates a rotating mechanical system mounting a brake disc-caliper group, which is driven by an electric motor, a pneumatic brake pedal assembly to simulate real braking conditions, and a comprehensive array of sensors that facilitate the measurement of critical parameters, such as rotation speed, braking torque, oil pressure, and disc temperature. Its structure, sensor integration, and control electronics are fully described, demonstrating the capability to replicate on-track scenarios in a controlled environment. The results underscore the utility of the bench in providing precise and consistent testing conditions essential for analyzing the efficiency, durability, and safety of the braking systems of FSAE race cars.
]]>Machines doi: 10.3390/machines12020136
Authors: Yuqian Yang Xin Chen Maolin Yang Wei Guo Pingyu Jiang
The Industrial Product Service System (IPS2) is considered a sustainable and efficient business model, which has been gradually popularized in manufacturing fields since it can reduce costs and satisfy customization. However, a comprehensive design method for IPS2 is absent, particularly in terms of requirement perception, resource allocation, and service activity arrangement of specific industrial fields. Meanwhile, the planning and scheduling of multiple parallel service activities throughout the delivery of IPS2 are also in urgent need of resolution. This paper proposes a method containing service order design, service resource configuration, and service flow modeling to establish an IPS2 for robot-driven sanding processing lines. In addition, we adopt the modified Deep Q-network (DQN) to realize a scheduling scheme aimed at minimizing the total tardiness of multiple parallel service flows. Finally, our industrial case study validates the effectiveness of our methods for IPS2 design, demonstrating that the modified deep reinforcement learning algorithm reliably generates robust scheduling schemes.
]]>Machines doi: 10.3390/machines12020134
Authors: Lei Song Chunguang Lu Chen Li Yongjin Xu Jiangming Zhang Lin Liu Wei Liu Xianbo Wang
With the rapid growth of the photovoltaic industry, fire incidents in photovoltaic systems are becoming increasingly concerning as they pose a serious threat to their normal operation. Research findings indicate that direct current (DC) fault arcs are the primary cause of these fires. DC arcs are characterized by high temperature, intense heat, and short duration, and they lack zero crossing or periodicity features. Detecting DC fault arcs in intricate photovoltaic systems is challenging. Hence, researching DC fault arcs in photovoltaic systems is of crucial significance. This paper discusses the application of mathematical morphology for detecting DC fault arcs. The system utilizes a multi-stage mathematical morphology filter, and experimental results have shown its effective extraction of fault arc features. Subsequently, we propose a method for detecting DC fault arcs in photovoltaic systems using a cyclic neural network, which is well-suited for time series processing tasks. By combining multiple features extracted from experiments, we trained the neural network and achieved high accuracy. This experiment demonstrates that our recurrent neural network (RNN) based scheme for DC fault arc recognition has significant reference value and implications for future research. The ROC curve on the test set approaches 1 from the initial state, and the accuracy on the test set remains at 98.24%, indicating the strong robustness of the proposed model.
]]>Machines doi: 10.3390/machines12020133
Authors: Wadah Talal Abdulrazzak Akroot
This study aims to develop, evaluate, and improve a polygeneration system that combines solar and Brayton cycle technologies and focuses on the sequential integration of heat. In this configuration, the exhaust gases from the Al-Qayyarah gas turbine power plant and the parabolic trough collector (PTC) array generate steam through a high recovery steam generation process. An absorption refrigeration system also supplies the Brayton circuit with low-temperature air. This process is evaluated from a 3E perspective, which includes exergy, energy, and exergoeconomic analyses for two different configurations. These configurations are integrated solar combined cycle (ISCC) with and without absorption systems (ISCC and ISCC-ARC). In addition, a comprehensive analysis was carried out to assess the impact of critical factors on the output generated, the unit cost of the products, and the exergy and energy efficiency for each configuration. The results revealed that the power produced by the ISCC-ARC and ISCC systems is 580.6 MW and 547.4 MW, respectively. Accordingly, the total energy and exergy efficiencies for the ISCC-ARC are 51.15% and 49.4%, respectively, while for the ISCC system, they are 50.89% and 49.14%, respectively. According to the results, the total specific costs for the ISCC-ARC system increased from 69.09 $/MWh in June to 79.05 $/MWh in December. ISCC’s total specific costs also fluctuate throughout the year, from 72.56 $/MWh in June to 78.73 $/MWh in December.
]]>Machines doi: 10.3390/machines12020132
Authors: Jaewook An Hamin Lee Chang-Wan Kim
In recent years, increased sales of fuel cell electric vehicles (FCEVs) have required composite overwrapped pressure vessel (COPV) designs to be lightweight and allow safe high-pressure hydrogen storage. In this study, we propose the weight minimization of Type 2 COPVs for FCEVs considering mechanical safety. Steel liner thickness, ply thickness, ply orientation, and the number of plies were set as design variables, and weight minimization was performed. For the constraints of optimization, the Tsai–Wu failure index of the composite layer and von Mises stress of the steel liner are considered. The design of experiments (DoE) was conducted to generate kriging model and perform sensitivity analysis. The optimized design of Type 2 COPVs was determined by satisfying all constraints, with significant weight reduction and preserved mechanical safety of the structure.
]]>Machines doi: 10.3390/machines12020131
Authors: Renato Brancati Domenico De Falco Giandomenico Di Massa Stefano Pagano Ernesto Rocca
Periodic monitoring of large industrial and civil structures is carried out through static and dynamic measurements. The monitoring, carried out over many years, offers important information for evaluating the health of structures and their management. Dynamic tests are carried out starting from measurements of the vibrations of the structure induced by mechanical devices or by the surrounding environment. If a ground support element is available, it is possible to exert a forcing action on the structure using actuators fixed to the support. When a ground support is unavailable, the structure can be forced using devices comprised of masses with rotary or reciprocating translational motion. These masses must be large enough to excite appreciable mechanical vibrations of the structure. In this paper, a vibration exciter, based on a mass suspended on an air spring and forced to vibrate at the resonant frequency, is proposed. Thanks to the resonant condition, the force transmitted to the structure is amplified compared to that applied to the mass. The excitation frequency can be adjusted by altering the inflation pressure of the air spring to modify the natural frequency of the system. In the paper, after the presentation of some mechanical devices used as vibration exciters for large structures, the proposed device is described and the first experimental results are reported.
]]>Machines doi: 10.3390/machines12020130
Authors: Austeja Dapkute Vytautas Siozinys Martynas Jonaitis Mantas Kaminickas Milvydas Siozinys
This study delves into the EA-SAS platform, a digital twin environment developed by our team, with a particular focus on the EA-SAS Cloud Scheduler, our bespoke program designed to optimize ETL (extract, transform, and load) scheduling and thereby enhance automation within industrial systems. We elucidate the architectural intricacies of the EA-SAS Cloud Scheduler, demonstrating its adeptness in efficiently managing computationally heavy tasks, a capability underpinned by our empirical benchmarks. The architecture of the scheduler incorporates Docker to create isolated task environments and leverages RabbitMQ for effective task distribution. Our analysis reveals the EA-SAS Cloud Scheduler’s prowess in maintaining minimal overhead times, even in scenarios characterized by high operational loads, underscoring its potential to markedly bolster operational efficiency in industrial settings. While acknowledging the limitations inherent in our current assessment, particularly in simulating real-world industrial complexities, the study also charts potential future research pathways. These include a thorough exploration of the EA-SAS Cloud Scheduler’s adaptability across diverse industrial scenarios and an examination of the integration challenges associated with its reliance on specific technological frameworks.
]]>