Machines, Volume 12, Issue 4 (April 2024) – 70 articles

To solve various reworking and repair problems caused by unqualified bearing product quality inspections, this paper introduces a green re-entrant scheduling optimization method for bearing production shops considering job reworking. By taking into account quality inspection constraints, this paper establishes an integrated scheduling mathematical model based on the entire processing–transportation–assembly process of bearing production shops with the goals for minimizing the makespan, total carbon emissions, and waste emissions. To solve these problems, the concepts of the set of the longest common machine routes (SLCMR) and the set of the shortest recombination machine combinations (SSRMC) were used to propose the re-entrant scheduling optimization method, based on system reconfiguration, to enhance the system stability and production scheduling efficiency. Then, a multi-objective hybrid optimization algorithm, based on a neighborhood local search (MOOA-LS), is proposed to improve the search scope and optimization ability by constructing a multi-level neighborhood search structure. Finally, this paper takes a bearing production shop as an example to carry out the case study and designs a series of experimental analyses and comparative tests. The final results show that in the bearing production process, the proposed model and algorithm can effectively realize green and energy-saving re-entrant manufacturing scheduling. Full article

Recently, due to the price fluctuation and supply instability of rare earth mineral resources, there has been a lot of development of electric motors using non-rare-earth permanent magnets. As a result, motors using Dy-free permanent magnets and ferrite permanent magnets are being researched, and, in particular, ferrite permanent magnets often utilize spoke-type structures, which are magnetic flux concentrators, to compensate for their low coercivity and residual flux density. However, in general, spoke-type PMSMs do not use much reluctance torque, so double-layer spoke-type PMSMs have been studied for their more efficient design. Unlike general spoke-type PMSMs, double-layer spoke-type PMSMs can utilize high reluctance torque by increasing the difference between d-axis and q-axis reluctance. However, as the difference in magnetic resistance increases, vibration and noise are generated, which adversely affects the mechanical part and shortens the life of the motor. Although this problem seemed to be solved by applying core skew in the previous study, it was confirmed that the axial force caused by the axial leakage flux occurred in the maximum torque per ampere (MTPA) control section and the torque ripple was increased. Therefore, in this paper, a model that can apply symmetrical core skew and reduce axial force is proposed. First, the causes of the axial force generated in previous studies were analyzed. Based on the analysis of these causes, a new symmetrical core skew structure was proposed, and its justification was verified through FEA. Full article

Artificial neural networks (ANNs) provide supervised learning via input pattern assessment and effective resource management, thereby improving energy efficiency and predicting environmental fluctuations. The advanced technique of ANNs forecasts diesel engine emissions by collecting measurements during trial sessions. This study included experimental sessions to establish technical and ecological indicators for a diesel engine across several operational scenarios. VALLUM01, a novel tool, has been created with a user-friendly interface for data input/output, intended for the purposes of testing and prediction. There was a comprehensive collection of 12 input parameters and 10 output parameters that were identified as relevant and sufficient for the objectives of training, validation, and prediction. The proper value ranges for transforming into fuzzy sets for input/output to an ANN were found. Given that the ANN’s training session comprises 1,000,000 epochs and 1000 perceptrons within a single-hidden layer, its effectiveness can be considered high. Many statistical distributions, including Pearson, Spearman, and Kendall, validate the prediction accuracy. The accuracy ranges from 96% on average, and in some instances, it may go up to 99%. Full article

When addressing the technical challenges of achieving precise force tracking during the local polishing process of polishing robots, controlling the contact state between the robot and the workpiece surface is essential. To this end, a contact motion-planning strategy based on dynamic systems is designed to generate trajectory routes during local polishing. The trajectory simulation of the local modulation dynamic system is achieved through the employment of the support vector regression (SVR) algorithm with a Gaussian kernel, facilitating the learning process. The feasibility and stability of planning local paths are validated using the local modulation dynamic system. To maintain a constant contact force between the end-effector polishing robot and the workpiece, an integral adaptive impedance control strategy is utilized, enabling the robot’s compliant control. Subsequently, an experimental system for the polishing robot is constructed in order to verify the effectiveness of the motion-planning system. The experimental results demonstrate that the proposed motion-planning approach is applicable in practical polishing processes, ensuring smooth contact and maintaining the desired contact force when scanning nonlinear surfaces, and thus showcasing stability and practicality. Full article

With the rapid expansion of offshore wind capacity worldwide, minimising operation and maintenance requirements is pivotal. Regarded as a low-maintenance alternative to conventional drivetrain systems, direct-drive generators are increasingly commonplace for wind turbines in hard-to-service areas. To facilitate higher torque requirements consequent to low-speed operation, these machines are bulky, greatly increasing nacelle size and mass over their counterparts. This paper therefore details the structural optimisation of the International Energy Agency 15 MW Reference Wind Turbine rotor through iterative Parameter and Topology Optimisation and the inclusion of additional structural members, with consideration to its mechanical, modal, and thermal performances. With temperature found to have a significant impact on the structural integrity of multi-megawatt direct-drive machines, a Computational Fluid Dynamics analysis was carried out to map the temperature of the structure during operation and inform a consequent Finite Element Method analysis. This process, novel to this paper, found that topologically optimised structures outperform parametrically optimised structures thermally and that integrated heatsinks can be employed to further reduce deformation. Lastly, generative design techniques were used to further optimise the structure, reducing its mass, deformation, and maximum stress and expanding its operating envelope. This study reaches several key conclusions, demonstrating that significant mass reductions are achievable through the removal of cylinder wall geometry areas as well as through the implementation of structural supports and iterative parametric and topology optimisation techniques. Through the flexibility it grants, generative design was found to be a powerful tool, delivering further improvements to an already efficient, yet complex design. Heatsinks were found to lower generator structural temperatures, which may yield lower active cooling requirements whilst providing structural support. Lastly, the link between the increased mass and the increased financial and environmental impact of the rotor was confirmed. Full article

The current network for the dual-task grinding wheel defect semantic segmentation lacks high-precision lightweight designs, making it challenging to balance lightweighting and segmentation accuracy, thus severely limiting its practical application in grinding wheel production lines. Additionally, recent approaches for addressing the natural class imbalance in defect segmentation fail to leverage the inexhaustible unannotated raw data on the production line, posing huge data wastage. Targeting these two issues, firstly, by discovering the similarity between Coordinate Attention (CA) and ASPP, this study has introduced a novel lightweight CA-ASP module to the DeeplabV3+, which is 45.3% smaller in parameter size and 53.2% lower in FLOPs compared to the ASPP, while achieving better segmentation precision. Secondly, we have innovatively leveraged the Masked Autoencoder (MAE) to address imbalance. By developing a new Hybrid MAE and applying it to self-supervised pretraining on tremendous unannotated data, we have significantly uplifted the network’s semantic understanding on the minority classes, which leads to further rises in both the overall accuracy and accuracy of the minorities without additional computational growth. Lastly, transfer learning has been deployed to fully utilize the highly related dual tasks. Experimental results demonstrate that the proposed methods with a real-time latency of 9.512 ms obtain a superior segmentation accuracy on the mIoU score over the compared real-time state-of-the-art methods, excelling in managing the imbalance and ensuring stability on the complicated scenes across the dual tasks. Full article

Autonomous underwater vehicles (AUVs) are now widely used in both civilian and military applications; however, wireless charging underwater often faces difficulties such as disturbances from ocean currents and errors in device positioning, making proper alignment of the charging devices challenging. Misalignment between the primary and secondary coils can significantly impact the efficiency and power of the wireless charging system energy transfer. To address the issue of misalignment in wireless charging systems, this paper proposes a multiple transfer coil wireless power transfer (MTCWPT) system based on backpropagation (BP) neural network control combined with nonsingular terminal sliding mode control (NTSMC) to enhance further the system robustness and efficiency. To achieve WPT in the ocean, a coil shielding case structure was equipped. In displacement experiments, the proposed multi-transmitting coil system could achieve stable power transfer of 40 W and efficiency of over 78.5% within a displacement range of 8 cm. The system robustness was also validated. This paper presents a new AUV energy supply solution based on MTCWPT. The proposed MTCWPT system can significantly improve the navigation performance of AUVs. Full article

Integral squeeze film damper (ISFD) is a new type of structure that appeared on the basis of traditional squeeze film damper (SFD). Since the oil film in ISFD is a segmented structure without annular flow, the nonlinearity of the oil film force has been improved to a great extent. The dynamic characteristic coefficients of ISFD have a close relationship with its damping performance. This work investigates and studies the dynamic characteristic parameters of ISFD by means of numerical analysis and experimental validation techniques in order to examine the dynamic features and unveil the damping mechanism. The ISFD solid and fluid analysis models are created, and the computational fluid dynamics (CFD) and mechanical performance analyses are completed. The force acting on the ISFD’s S-type elastomer under excitation conditions is revealed in the mechanical property analysis, and the flow characteristics of the internal oil film are investigated in the CFD analysis. It is discovered that the ISFD has good linear damping and stiffness characteristics, and numerical analytical values for the ISFD’s damping and stiffness coefficients are obtained. By constructing a bi-directional excitation test rig, the experimental values of the ISFD stiffness coefficient and damping coefficient are determined. These values are in close agreement with the results of the numerical analysis, confirming the accuracy of the ISFD’s numerical analysis conclusions. Full article

Orthoplanar springs are single-component compliant mechanisms that can be fabricated from sheet material and undergo deflection orthogonal to the plane of the mechanism. They are useful in applications where spatial constraints are significant. An Euler spiral is a curve whose curvature is linearly proportional to the arc length allowing for the curve to assume a flat position under a load. In this work, orthoplanar spring and Euler-spiral concepts are synthesized to create a single-component spring mechanism that lies flat under a load. Where traditional planar springs under a load will take on an out-of-plane contour, the Euler-spiral orthoplanar spring lies completely flat under a load. The relationship between the load needed to flatten the orthoplanar Euler-spiral spring and its physical geometry is examined. A use case where the Euler-spiral orthoplanar spring is utilized as a deployment mechanism for a mid-flight emerging antenna on the surface of a flight body is presented. Full article

The need for industrial and commercial machinery to maintain high torque while accurately following a variable angular speed is increasing. To meet this demand, induction motors (IMs) are commonly used with variable speed drives (VSDs) that employ a field-oriented control (FOC) scheme. Over the last thirty years, IMs have been replacing independent connection direct current motors due to their cost-effectiveness, reduced maintenance needs, and increased efficiency. However, IMs and VSDs exhibit nonlinear behavior, uncertainties, and disturbances. This paper proposes a robust combined adaptive passivity-based control (CAPBC) for this class of nonlinear systems that applies to angular rotor speed and stator current regulation inside an FOC scheme for IMs’ VSDs. It uses general Lyapunov-based design energy functions and adaptive laws with $\sigma$-modification to assure robustness after combining control and monitoring variables. Lyapunov’s second method and the Barbalat Lemma prove that the control and identification error tends to be zero over time. Moreover, comparative experimental results with a standard proportional–integral controller (PIC) and direct APBC show the proposed CAPBC’s effectiveness and robustness under normal and changing conditions. Full article

A liquid nitrogen cooling circulating unit is a necessary condition for the stable operation of a cryogenic oscillator, which can provide a stable working environment for the oscillator. In this paper, according to the user’s functional requirements and performance parameters, a closed cooling system with supercooled liquid nitrogen as the medium was designed using SOLIDWORKS 2021 software, which can provide a suitable working environment for the cryogenic oscillator. Combined with the system heat load analysis, theoretical calculation for and the design of the coil heat exchanger, one of the core pieces of equipment of the unit, were carried out. The performance of the designed nitrogen exhaust heater was studied using FLUENT 2021 software, and the velocity field and temperature field of the nitrogen exhaust heater were analyzed. The results show that the outlet temperature of the nitrogen exhaust heating device can reach up to 310 K, and the outlet flow rate of the heating device is 0.01528 kg/s. The experiments on the liquid nitrogen circulating unit using the simulated load equipment show that the refrigeration power of the unit can reach a design index of 600 W, and the temperature of the liquid nitrogen at the liquid outlet of the unit can reach 77.8 K. The experiments also show that the unit meets the design requirements. Full article

Active suspension control technologies have become increasingly significant in improving suspension performance for driving stability and comfort. An RBF-based fractional-order SMC fault-tolerant controller is developed in this research to guarantee ride comfort and handling stability when faced with the partial loss of actuator effectiveness due to failure. To obtain better control performance, fractional-order theory and the RBF algorithm are discussed to solve the jitter vibration problem in SMC, and the RBF is exploited to obtain a more appropriate switching gain. First, a half-nonlinear active suspension model and a fault car model are presented. Then, the design process of the RBF-based fractional-order SMC fault-tolerant controller is described. Next, a simulation is presented to demonstrate the effectiveness of the proposed strategy. According to the simulation, the proposed method can improve performance in the case of a healthy suspension, and the fault-tolerant controller can guarantee the capabilities when actuators go wrong. Full article

A model based on a three-dimensional fractal function is developed and used in conjunction with experiments to analyze the evolutionary pattern of sealing performance during the start-up process of dynamic pressure seals, and the influence of end-face microscopic features on the evolution law is discussed. It is found that the opening state of the seal is divided into three stages: the non-opened stage, transition stage, and full-opened stage. The isotropic dimensions of the cavities have a coupling effect on the leakage, and they diminish as the speed increases. In order to enhance the sealing performance during start-up, it is suggested that the seal faces have a fractal dimension of 2.4 to 2.6, and a characteristic factor of less than 1 × 10⁻⁹ m. Full article

Hydropower generators withstand multiple forces from diverse sources during operation. To ensure their stability and safe performance, numerical tools are developed to characterize their dynamic properties. Traditionally, generators are assumed to be rigid in rotordynamic analyses. However, the measurements in power stations challenge this assumption. This article proposes a novel approach to modeling hydropower generators with floating rotor rims using a three-dimensional (3-D) Finite Element Method, aiming to study their dynamic performance and properties, including natural frequencies, the modes of vibrations, and expansion due to centrifugal and electromagnetic forces, with the goal of improving the reliability of modern designs. Both this approach and employing a two-dimensional (2-D) model using curved beams result in similar in-plane natural frequencies and the expansion of the rotor rim due to centrifugal forces. However, the 3-D model can be used to calculate the out-of-plane natural frequencies and modes, to model the dynamics of complex geometries, and to perform stress evaluation and fatigue analysis. Full article

This study investigates the effect of the elastic surface micro-texture on the cavitation and lubrication characteristics of the friction pairs through theoretical and experimental research. Through numerical simulations and experiments, the influences of the elastic modulus and sliding speed on the lubrication performance of the friction pair are studied. The results show that under certain speed and load conditions, the friction coefficient of the elastic texture is smaller, and the lubrication performance is better than that of the rigid texture. Increasing the sliding speed and texture spacing properly can improve the lubrication performance of elastic friction pairs. In addition, as the elastic modulus decreases, the elastic deformation and oil film thickness increase, and the cavitation phenomenon becomes more significant. Thus, the lubrication performance of the friction pair is improved. Full article

Sliding bearings are vital components in modern industry, exerting a crucial influence on equipment performance, with wear being one of their primary failure modes. In addressing the issue of wear diagnosis in sliding bearings, this paper proposes an intelligent diagnostic method based on a multiscale gated convolutional neural network (MGCNN). The proposed method allows for the quantitative inference of the maximum wear depth (MWD) of sliding bearings based on online vibration signals. The constructed model adopts a dual-path parallel structure in both the time and frequency domains to process bearing vibration signals, ensuring the integrity of information transmission through residual network connections. In particular, a multiscale gated convolution (MGC) module is constructed, which utilizes convolutional network layers to extract features from sample sequences. This module incorporates multiple scale channels, including long-term, medium-term, and short-term cycles, to fully extract information from vibration signals. Furthermore, gated units are employed to adaptively assign weights to feature vectors, enabling control of information flow direction. Experimental results demonstrate that the proposed method outperforms the traditional CNN model and shallow machine learning model, offering promising support for equipment condition monitoring and predictive maintenance. Full article

The purpose of this work is to study the mechanism of running-in during friction and to determine the informative parameters characterizing the degree of its completion. During friction, contact interaction of rough surfaces causes various wave phenomena covering a wide range of frequencies, the subsequent frequency analysis can provide information about the sizes of wave sources and thereby clarify the mechanism of interaction between surface roughness. The using of the wavelet transform for processing the signals of audible acoustic emission made it possible to determine the beginning and the end of the change in the frequency ranges of the interaction of roughness. The code developed by the authors was used to analyze the acoustic emission signals by using wavelet energy and entropy criteria. The mother wavelet was chosen by carefully evaluating the effectiveness of 54 preliminary candidates for the mother wavelet from 7 wavelet families, according to three criteria: (1) maximum wavelet energy; (2) Shannon entropy minimum; and (3) maximum energy-to-Shannon entropy ratio. Full article

Most of the current studies on autonomous vehicle decision-making and control based on reinforcement learning are conducted in simulated environments. The training and testing of these studies are carried out under the condition of rule-based microscopic traffic flow, with little consideration regarding migrating them to real or near-real environments. This may lead to performance degradation when the trained model is tested in more realistic traffic scenes. In this study, we propose a method to randomize the driving behavior of surrounding vehicles by randomizing certain parameters of the car-following and lane-changing models of rule-based microscopic traffic flow. We trained policies with deep reinforcement learning algorithms under the domain-randomized rule-based microscopic traffic flow in freeway and merging scenes and then tested them separately in rule-based and high-fidelity microscopic traffic flows. The results indicate that the policies trained under domain-randomized traffic flow have significantly better success rates and episodic rewards compared to those trained under non-randomized traffic flow. Full article

In this study, a novel cambered snow removal device is designed to achieve automatic snow removal in large curved areas, such as the south roof of a Chinese solar greenhouse. The theory of structural parameters and shear force is ambiguous. People are not based on the greenhouse structure parameters for the selection of snow removal devices. Therefore, the quantitative relationship between the structure of the greenhouse span and the number of scissor arm-length knots is analysed, and the relationship between the material strength and application distance is determined. This study’s objectives are (1) to establish a theoretical model of scissor arm motion and (2) to analyse the force distribution of the scissor arm using multi-body dynamics. The results show that the scissor arm of a round-arch greenhouse has fewer sections but a larger arm length, whereas the scissor arm of a traditional solar greenhouse has more sections but a smaller arm length. Based on the shear force of the scissor structure, the optimised wall thickness reduces the force of the node by 17%. Full article

In nuclear-powered steam turbines, the aerodynamic performance of the inlet chamber tends to be contradictory to the compact design, which makes it difficult to achieve optimal efficiency and power in a nuclear-powered steam turbine. In this study, the quantitative correlation between compact design and aerodynamic performance was investigated to reveal the interaction mechanism between the aerodynamic performance of the inlet chamber of the steam turbine with its compactness. First, the effects of peripheral quantity and arrangement of inlets in the chamber inlet on its aerodynamic performance were studied. The results indicated that the proposed cross configuration exhibited optimized aerodynamic performance in multiple aspects. Then, the effects of two compactness indices (inlet/outlet area ratio and axial spacing at outlet) on the aerodynamic performance of the inlet chamber were investigated. The results indicated that the volume of the inlet chamber was proportional to the inlet/outlet area ratio, and an appropriate design of the inlet chamber can achieve compactness without significantly affecting its aerodynamic performance. In addition, the influencing mechanism of the compact optimization design on the aerodynamic performances of the inlet chamber was revealed. This study provides references for optimization designing an effective and compacted inlet chamber of steam turbines. Full article

Equivalent combustion natural gas engines typically utilize exhaust gas recirculation (EGR) systems to tackle their high thermal burden and NOx emissions. Variable nozzle turbochargers (VNT) can increase the engine intake and EGR rate simultaneously, resulting in NOx reduction while ensuring robust power performance. Using a VNT along with engine bench testing, the impact of VNT- and EGR-coordinated control on the performance and emissions of equivalent combustion natural gas engines was investigated under different operating conditions. Subsequently, multi-objective optimization was performed using a support vector machine. The results demonstrated that the use of VNTs in equivalent combustion natural gas engines could bolster the capacity to introduce EGR under several operative conditions and extend the scope of EGR regulation, thereby decreasing the engine’s thermal burden, improving fuel efficiency, and curbing emissions. Owing to the implementation of a multi-objective optimization method based on a support vector regression model and NSGA-II genetic algorithm, VNT and EGR control parameters could be optimized to slightly improve the economy and significantly reduce NOx emissions while maintaining the original engine power performance. At 20 operating points optimized for validation, brake-specific fuel consumption (BSFC) and NOx decreased by 0.94% and 47.0%, respectively, and CH₄ increased by 3.7%, on average. Full article

Efficient gearbox fault diagnosis is crucial for the cost-effective maintenance and reliable operation of rotating machinery. Despite extensive research, effective fault diagnosis remains challenging due to the multitude of features available for classification. Traditional feature selection methods often fail to achieve optimal performance in fault classification tasks. This study introduces diverse ranking methods for selecting the relevant features and utilizes data segmentation techniques such as sliding, windowing, and bootstrapping to strengthen predictive model performance and scalability. A comparative analysis of these methods was conducted to identify the potential causes and future solutions. An evaluation of the impact of enhanced feature engineering and data segmentation on predictive maintenance in gearboxes revealed promising outcomes, with decision trees, SVM, and KNN models outperforming others. Additionally, within a fully connected network, windowing emerged as a more robust and efficient segmentation method compared to bootstrapping. Further research is necessary to assess the performance of these techniques across diverse datasets and applications, offering comprehensive insights for future studies in fault diagnosis and predictive maintenance. Full article

During the polishing process of complex curved PMMA parts, the polishing force is an important factor affecting the surface quality and optical performance. In this paper, a force-controlled polishing device integrated into a machining center to maintain the polishing force is investigated. In order to achieve the real-time active control of the polishing force, the linear voice coil motor and force sensors are used for motion and measurement. A compact structure was designed to couple the linear motion of the voice coil motor with the rotation for polishing. The force-controlled polishing system with a high real-time hardware architecture was developed to perform complex curved polishing path movement with precise force control. Next, the polishing force between the device and the workpiece was analyzed to obtain the mathematical model of the device. Considering the impact during the approaching phase of polishing, a fuzzy PI controller was proposed to reduce the overshoot and response time. To implement the control method, the controller model was established on Simulink and the control system was developed based on TwinCAT 3 software with real-time computing capability. Finally, a polishing experiment involving a complex curved PMMA part was conducted by a force-controlled polishing device integrated into a five-axis machining center. The results show that the device can effectively maintain the polishing force to improve surface quality and optical performance. Full article

In engineering practice, anuloid surfaces are produced that are subject to strict requirements in terms of accuracy (e.g., in the bearing industry). Roundness is also often measured. The accuracy of the roundness measurement does not only depend on the accuracy of a particular metrological system. The setting of the component on the measuring device can also have a significant influence on the accuracy of the measurement. The axis of the measured surface must be perpendicular to the section in which the roundness is measured. Tilting the measured surface causes a systematic measurement error, which depends on the size of the tilt angle and the parameters of the measured surface. This study presents mathematical relations for the calculation of this error when measuring roundness on an anuloid surface. Mathematical relations for the internal and external surfaces formed by the inner and outer parts of the anuloid are derived herein. There is also a theoretical analysis of the influence of inclination and the parameters of the measured surface on the size of this error. Theoretically calculated values were compared with practically measured values of roundness. The error had a negative effect on the accuracy of the roundness measurement. It affected the roundness value, the second harmonic component, and also the roundness profile itself. Full article

The measurement of the ground forces on a real structure or mechanism in operation can be time-consuming and expensive, particularly when production cannot be halted to install sensors. In cases in which disassembling the parts of the system to accommodate sensor installation is neither feasible nor desirable, observing the structure or mechanism in operation and quickly deducing its force trends would facilitate monitoring activities in industrial processes. This opportunity is gradually becoming a reality thanks to the coupling of artificial intelligence (AI) with design techniques such as the finite element and multi-body methods. Properly trained inferential models could make it possible to study the dynamic behavior of real systems and mechanisms in operation simply by observing them in real time through a camera, and they could become valuable tools for investigation during the operation of machinery and devices without the use of additional sensors, which are difficult to use and install. In this paper, the idea presented is developed and applied to a simple mechanism for which the reaction forces during operating conditions are to be determined. This paper explores the implementation of an innovative vision-based virtual sensor that, through data-driven training, is able to emulate traditional sensing solutions for the estimation of reaction forces. The virtual sensor and relative inferential model is validated in a scenario as close to the real world as possible, taking into account interfering inputs that add to the measurement uncertainty, as in a real-world measurement scenario. The results indicate that the proposed model has great robustness and accuracy, as evidenced by the low RMSE values in predicting the reaction forces. This demonstrates the model’s effectiveness in reproducing real-world scenarios, highlighting its potential in the real-time estimation of ground reaction forces in industrial settings. The success of this vision-based virtual sensor model opens new avenues for more robust, accurate, and cost-effective solutions for force estimation, addressing the challenges of uncertainty and the limitations of physical sensor deployment. Full article

When faced with challenges such as adapting to dynamic environments and handling ambiguous identification, indoor service robots encounter manifold difficulties. This paper aims to address this issue by proposing the design of a service robot equipped with precise small-object recognition, autonomous path planning, and obstacle-avoidance capabilities. We conducted in-depth research on the suitability of three SLAM algorithms (GMapping, Hector-SLAM, and Cartographer) in indoor environments and explored their performance disparities. Upon this foundation, we have elected to utilize the STM32F407VET6 and Nvidia Jetson Nano B01 as our processing controllers. For the program design on the STM32 side, we are employing the FreeRTOS operating system, while for the Jetson Nano side, we are employing ROS (Robot Operating System) for program design. The robot employs a differential drive chassis, enabling successful autonomous path planning and obstacle-avoidance maneuvers. Within indoor environments, we utilized the YOLOv3 algorithm for target detection, achieving precise target identification. Through a series of simulations and real-world experiments, we validated the performance and feasibility of the robot, including mapping, navigation, and target detection functionalities. Experimental results demonstrate the robot’s outstanding performance and accuracy in indoor environments, offering users efficient service and presenting new avenues and methodologies for the development of indoor service robots. Full article

Injection moulds are crucial to produce plastic and lightweight metal components. One primary associated challenge is that these may suffer from different types of failures, such as wear and/or cracking, due to the extreme temperatures (T), thermal cycles, and pressures involved in the production process. According to the intended geometry and respective needs, mould manufacturing can be performed with conventional or non-conventional processes. This work focuses on three foremost alloys: AMPCO^® (CuBe alloy), INVAR-36^® (Fe-Ni alloys, Fe-Ni36), and heat-treated (HT) steels. An insight into the manufacturing processes’ limitations of these kinds of materials will be made, and solutions for more effective machining will be presented by reviewing other published works from the last decade. The main objective is to provide a concise and comprehensive review of the most recent investigations of these alloys’ manufacturing processes and present the machinability challenges from other authors, discovering the prospects for future work and contributing to the endeavours of the injection mould industry. This review highlighted the imperative for more extensive research and development in targeted domains. Full article

This paper presents a method for designing an active suspension controller for ride comfort enhancement and motion sickness mitigation. For this, it is necessary to design an active suspension controller, which aims to reduce the vertical acceleration and pitch rate of a sprung mass in a vehicle. A half-car vehicle model was selected. For the controller design, a static output feedback (SOF) control was selected instead of a full-state feedback control because it is hard to measure all state variables in real vehicles. With the available signals, three types of SOF controller were proposed. To determine the gains of the SOF controllers, a linear quadratic optimal control methodology and a simulation-based optimization method were adopted. To validate the proposed method, a simulation was carried out using vehicle simulation software. The simulation results show that the proposed method is quite effective for ride comfort enhancement and motion sickness mitigation. Full article

Additive manufacturing techniques offer significant advantages for creating complex components efficiently, saving both time and materials. This makes them particularly appealing for producing parts from intricate alloys, such as Hastelloy C-22. One such technique, plasma metal deposition, uses plasma on powdered material to build up layers. The novelty of this work is to analyze and determine whether there is a correlation between the particle size and the final behaviour of specimens produced via additive manufacturing. To achieve this, four powders with an identical chemical composition but different granulometries were employed. Additionally, some of the samples underwent thermal treatment (progressive heating at 10 °C/min until 1120 °C, maintained for 20 min, followed by rapid air cooling). Four walls were constructed, and after mechanical, tribological, and microstructural characterization, it was determined that the influence of the thermal treatment remained consistent, regardless of particle size. It was observed that the particle size slightly affected the final properties: the finer the powder, the lower the ultimate tensile strength values. Furthermore, it was evident that the thermal treatment substantially affected the microstructure and wear behavior of all the specimens, regardless of their initial particle size. Full article

Autonomous lane-change, a key feature of advanced driver-assistance systems, can enhance traffic efficiency and reduce the incidence of accidents. However, safe driving of autonomous vehicles remains challenging in complex environments. How to perform safe and appropriate lane change is a popular topic of research in the field of autonomous driving. Currently, few papers consider the safety of reinforcement learning in discretionary lane-change scenarios. We introduce safe hybrid-action reinforcement learning into discretionary lane change for the first time and propose the Parameterized Soft Actor–Critic with PID Lagrangian (PASAC-PIDLag) algorithm. Furthermore, we conduct a comparative analysis with Parameterized Soft Actor–Critic (PASAC), which is an unsafe version of PASAC-PIDLag. Both algorithms are employed to train the lane-change strategy to output both discrete lane-change decisions and continuous longitudinal vehicle acceleration. Our simulation results indicate that at a traffic density of 15 vehicles per kilometer (15 veh/km), the PASAC-PIDLag algorithm exhibits superior safety with a collision rate of 0%, outperforming the PASAC algorithm, which has a collision rate of 1%. The generalization assessments reveal that at low traffic density levels, both the PASAC-PIDLag and PASAC algorithms are proficient in attaining zero collision rates. However, at high traffic density levels, although both algorithms result in collisions, PASAC-PIDLag has a much lower collision rate than PASAC. Full article