Editor’s Choice Articles

At the conceptual design stage, it is critical to use appropriate structural analysis and optimization methods. The thin-walled beam transfer matrix method (TBTMM) is adopted to establish the mathematical model of the simplified vehicle body-in-white (BIW) structure in this paper and compare it with the results of the finite element method (S-FEM) to verify the approach. In addition, on the basis of the boundary simulation genetic algorithm (BSGA) and local search procedure, a boundary hybrid genetic algorithm (BHGA) is proposed. BHGA is benchmarked on 20 test functions and is compared with current meta-heuristic algorithms to prove its effectiveness and universality. Finally, considering the bending and torsional stiffness constraints, BIW conceptual model is lightweight and designed with an optimizer. Full article

Large-scale components usually have complex structures with high local stiffness, and the holes on them are required to be machined with high precision, which makes it important and challenging to study how to efficiently and precisely drill in the large-scale components. This article presents mobile hybrid machining equipment that consists of a five-axis parallel module, a 2-degree-of-freedom (DoF) robotic, arm and an automated guide vehicle (AGV) connected in series. With the ability of wide-range positioning and precise local processing, it has potential advantages in the drilling processing of large-scale components. Stiffness is one of the most important performances for machining equipment, and it’s highly related to the its configuration. For the discussed equipment, the stiffness is determined by the two-stage-positioning hybrid machining robot, which comprises a five-axis parallel module and a two-DoF robotic arm. The redundant motion of the hybrid machining robot makes it possible to optimize its configuration by reasonably planning redundant motion. Therefore, a redundant motion-planning method based on stiffness performance is proposed. A kinematic analysis of the five-axis parallel module, the robotic arm, and the hybrid machining robot is carried out. A hybrid robot usually consists of several subsystems, and to take the compliance of each subsystem into consideration, the stiffness-modeling method for the hybrid robot with n subsystems connected in series is proposed. The stiffness model of the hybrid machining robot is established by using this method, and the variation of the stiffness magnitude has the same trend as that obtained by using FEA software. Stiffness magnitude and isotropy indices are proposed to evaluate the robot’s stiffness performance along the axis of the spindle and in the plane perpendicular to the axis of the spindle. The redundant motion of the hybrid machining robot is planned by maximizing the stiffness magnitude along the spindle axis, with priority to the stiffness isotropy index. Finally, the drilling experiment is carried out, and the results show that the relative error of the hole diameter obtained under the optimal configuration of the hybrid machining robot is 1.63%, which is smaller than those obtained under the other two configurations for comparison with relative errors of 3.75% and 3.50%, respectively. It proves the validity of the redundant motion-planning method. The proposed stiffness-modeling method and the stiffness-performance indices are also applicable to other hybrid machining robots. Full article

A vision-based intelligent robotic grasping system is essential for realizing unmanned operations in industrial manufacturing, and pose estimation plays an import role in this system. In this study, deep learning was used to obtain the 6D pose of an axisymmetric body which was optimal for robotic grasping in industrial stacked scenarios. We propose a method to obtain the 6D pose of an axisymmetric body by detecting the pre-defined keypoints on the side surface. To realize this method and solve other challenges in industrial stacked scenarios, we propose a multitask real-time convolutional neural network (CNN), named Key-Yolact, which involves object detection, instance segmentation, and multiobject 2D keypoint detection. A small CNN as a decision-making subsystem was designed to score multiple predictions of Key-Yolact, and the body with the highest score is considered the best for grasping. Experiments on a self-built stacked dataset showed that Key-Yolact has a practical tradeoff between inference speed and precision. The inference speed of Key-Yolact is higher by 10 FPS, whereas its precision is decreased by only 7% when compared with the classical multitask Keypoint R-CNN. Robotic grasping experiments showed that the proposed design is effective and can be directly applied to industrial scenarios. Full article

Preprogrammed pulse width modulation (PPWM) techniques are drawing a great deal of interest due to their strong harmonic performance. However, there have not yet been any systematic studies or elaboration of the influence of different grid powers on current and voltage THD using PPWM. Therefore, this article focuses on a comparative analysis of current and voltage THD in a system with a three-phase, three-level active front-end (AFE) at different grid powers by applying PPWM. A six-pulse electric drive power circuit and one laboratory measurement platform were designed and set up to achieve the above goals. The comparative results were calculated up to the 50th (THD₅₀) and 100th (THD₁₀₀) harmonics against the frequency of the PPWM, ranging between 150 Hz and 750 Hz. The grid power and AFE power ratio was between 30 and 230 under three different AFE-consumed currents. The experimental results were analyzed and compared, and they demonstrated for the first time how the grid power and AFE power ratio with different PPWM patterns can influence current and voltage THD. The research results suggest that it is necessary to review the existing calculation methods for current and voltage THD using modern electric power quality standards. The results presented in this article also provide a reference point for researchers and engineers when considering the electromagnetic compatibility (EMC) of nonlinear consumers in the design of similar circuits. Full article

The research on rehabilitation robots is gradually moving toward combining human intention recognition with control strategies to stimulate user involvement. In order to enhance the interactive performance between the robot and the human body, we propose a machine-learning-based human motion intention recognition algorithm using sensor information such as force, displacement and wheel speed. The proposed system uses the bi-directional long short-term memory (BILSTM) algorithm to recognize actions such as falling, walking, and turning, of which the accuracy rate has reached 99.61%. In addition, a radial basis function neural network adaptive sliding mode controller (RBFNNASMC) is proposed to track and control the patient’s behavioral intention and the gait of the lower limb exoskeleton and to adjust the weights of the RBF network using the adaptive law. This can achieve a dynamic estimation of the human–robot interaction forces and external disturbances, and it gives the exoskeleton joint motor a suitable driving torque. The stability of the controller is demonstrated using the Lyapunov stability theory. Finally, the experimental results demonstrate that the BILSTM classifier has more accurate recognition than the conventional classifier, and the real-time performance can meet the demand of the control cycle. Meanwhile, the RBFNNASMC controller has a better gait tracking effect compared with the PID controller. Full article

Eco-cruising is considered an effective approach for reducing energy consumption of connected vehicles. Most eco-cruising controllers (ECs) do not comply with real-time implementation requirements when a short sampling interval is required. This paper presents a solution to this problem. Model predictive control (MPC) framework was applied to the speed-planning problem for a power-split hybrid electric vehicle (HEV). To overcome the limitations of time-domain MPC (TMPC), a nonlinear space-domain MPC (SMPC) was proposed in the space domain. A real-time iteration (RTI) algorithm was developed to accelerate nonlinear SMPC computations via generating warm initializations and subsequently forming the SMPC-RTI. Proposed speed controllers were evaluated in a hierarchical EC, where a heuristic energy management strategy was selected for powertrain control. Simulation results indicated that the proposed SMPC yields comparable fuel savings to the TMPC and the globally optimal solution. Meanwhile, SMPC reduced MPC computation time by 41% compared to TMPC, and SMPC-RTI further reduced MPC computation time without compromising optimization. During the hardware-in-loop (HIL) test, the mean computation time was 9.86 ms, demonstrating potential for real-time applications. Full article

Rotating machine vibration signals typically represent a large collection of responses from various sources in a machine, along with some background noise. This makes it challenging to precisely utilise the collected vibration signals for machine fault diagnosis. Much of the research in this area has focused on computing certain features of the original vibration signal in the time domain, frequency domain, and time–frequency domain, which can sufficiently describe the signal in essence. Yet, computing useful features from noisy fault signals, including measurement errors, needs expert prior knowledge and human labour. The past two decades have seen rapid developments in the application of feature-learning or representation-learning techniques that can automatically learn representations of time series vibration datasets to address this problem. These include supervised learning techniques with known data classes and unsupervised learning or clustering techniques with data classes or class boundaries that are not obtainable. More recent developments in the field of computer vision have led to a renewed interest in transforming the 1D time series vibration signal into a 2D image, which can often offer discriminative descriptions of vibration signals. Several forms of features can be learned from the vibration images, including shape, colour, texture, pixel intensity, etc. Given its high performance in fault diagnosis, the image representation of vibration signals is receiving growing attention from researchers. In this paper, we review the works associated with vibration image representation-based fault detection and diagnosis for rotating machines in order to chart the progress in this field. We present the first comprehensive survey of this topic by summarising and categorising existing vibration image representation techniques based on their characteristics and the processing domain of the vibration signal. In addition, we also analyse the application of these techniques in rotating machine fault detection and classification. Finally, we briefly outline future research directions based on the reviewed works. Full article

This paper presents a review of the recent trends and the current state of the art in the application of fiber optic fiber Bragg gratings (FBG) sensing technology to condition the monitoring (CM) and testing of practical electric machinery and the associated power equipment. FBG technology has received considerable interest in this field in recent years, with research demonstrating that the flexible, multi-physical, and electromagnetic interference (EMI) immune in situ sensing of a multitude of physical measurands of CM interest is possible and cannot be obtained through conventional sensing means. The unique FBG sensing ability has the potential to unlock many of the electric machine CM and design validation restrictions imposed by the limitations of conventional sensing techniques but needs further research to attain wider adoption. This paper first presents the fundamental principles of FBG sensing. This is followed by a description of recent FBG sensing techniques proposed for electric machinery and associated power equipment and a discussion of their individual benefits and limitations. Finally, an outlook for the further application of this technique is presented. The underlying intention is for the review to provide an up-to-date overview of the state of the art in this area and inform future developments in FBG sensing in electric machinery. Full article

In this paper, we present a non-destructive testing (NDT) technique based on in situ detection of defects up to 100 °C by ultrasonic testing (UT) during construction of parts by a metal additive manufacturing technology known as wire arc additive manufacturing (WAAM). The proposed technique makes use of interlayer application of commercial solder flux to serve as coupling medium for in situ inspection using a special-purpose UT probe. The experimental work was carried out in deposited ER5356 aluminum straight walls following a threefold structure. First, characterization tests with geometrically similar walls with and without interlayer application of solder flux highlight its neutrality, with no effect on the chemical, metallurgical and mechanical properties of the walls. Secondly, UT tests on walls at temperatures ranging from room temperature to 100 °C demonstrate the satisfactory performance of the solder flux as a coupling medium, with little to no soundwave amplitude losses or noise. Finally, acoustic attenuation, impedance and transmission estimations highlight the effectiveness of the proposed technique, establishing a basis for the future development of automated NDT systems for in situ UT of additive manufacturing processes. Full article

In this paper, an energy consumption generation method is proposed to accurately calculate the energy consumption of fuel cell vehicles (FCVs). A specific driver drives on a route (from Jilin University to FAW Volkswagen) for 331 working days (1 April 2020 to 28 July 2021) and collects more than 40,000 s of naturalistic driving data by means of a GPS receiver (FRII-D). To accurately calculate the energy consumption data of FCVs under actual driving cycles, naturalistic driving data mining is first studied. The principal component analysis (PCA) algorithm is used to reduce the dimension of the extracted driving cycle characteristic parameters, the K-means algorithm is used for driving cycle clustering, and the LVQ is used for driving cycle identification. Then, the characteristic parameters correlated to energy consumption are obtained based on the FCV model and regression analysis method. In addition, an energy consumption generation method is designed and proposed based on the characteristic parameters and identification results. Furthermore, the proposed energy consumption generation method can accurately calculate the energy consumption of FCVs, which also provides a reference for further research on the efficient energy management of FCVs. Full article

Existing machine vision-based roughness measurement methods cannot accurately measure the roughness of free-form surfaces (with large curvature variations). To overcome this problem, this paper proposes a roughness measurement method based on a generative adversarial network (GAN) and a BP neural network. Firstly, this method takes images and curvature of free-form surfaces as training samples. Then, GAN is trained for roughness measurement through each game between generator and discriminant network by using real samples and pseudosamples (from generator). Finally, the BP neural network maps the image discriminant value of GAN and radius of curvature into roughness value (Ra). Our proposed method automatically learns the features in the image by GAN, omitting the independent feature extraction step, and improves the measurement accuracy by BP neural network. The experiments show that the accuracy of the proposed roughness measurement method can measure free-form surfaces with a minimum roughness of 0.2 μm, and measurement results have a margin of 10%. Full article

Traditional methods for solving the inverse kinematics of a hyper-redundant manipulator (HRM) can only plan the path of the end-effector with a complicated solving process, where obstacle avoidance is also not considered. To solve the above problems, a novel method for solving inverse kinematics of HRM is proposed in this paper: the variable dimension scaling method (VDSM), which can solve complex inverse kinematics while avoiding obstacles. Through this method, the path of the end-effector is scaled under a certain proportion and is adjusted depending on the position of the obstacle, which has good universality. The number of link angles changed is as small as possible in the process of achieving the end-effector moving along the desired path. With the redundancy of HRM, obstacle avoidance can be implemented in any environment by the proposed method. Through simulation and experiments in different environments, the above advantages of VDSM are verified. Full article

The main transmission system of wind turbines is a multi-component coupling system, and its operational state is complex and varied. These lead to frequent false alarms and missed alarms in existing monitoring systems. To accurately obtain the operational state of the main transmission system and detect its abnormal operation, an early fault warning method for the main transmission system based on SCADA and CMS data is proposed. Firstly, the SCADA and CMS feature parameters relevant to the operating status of the main transmission system are selected by two different methods separately, and the correlation mechanism between the feature parameters and the operating characteristics of the main transmission system is further analyzed. Secondly, the Long Short-Term Memory (LSTM) network-based prediction model of the main transmission system operating parameters is established, in which SCADA and CMS feature parameters are fused as the input feature vectors. Then, the predicted residuals of the state evaluation parameters are used as the operational state evaluation index. The early fault warning model is established by Analytic Hierarchy Process (AHP) and Kernel Density Estimation (KDE). Finally, a case study is used to verify the correct performance of the proposed method. The results show that this method can realize early warning functions 73 h earlier than the existing SCADA system. The method can provide a theoretical basis for the safe operation and condition-based maintenance of wind turbines. Full article

For a 2UPR-RPU over-constrained parallel manipulator, some geometric errors result in internal forces and deformations, which limit the improvement of the pose accuracy of the moving platform and shorten the service life of the manipulator. Analysis of these geometric errors is important for restricting them. In this study, an evaluation model is established to analyse the influence of geometric errors on the limbs’ comprehensive deformations for this manipulator. Firstly, the nominal inverse and actual forward kinematics are analysed according to the vector theory and the local product of the exponential formula. Secondly, the evaluation model of the limbs’ comprehensive deformations is established based on kinematics. Thirdly, 41 geometric errors causing internal forces and deformations are identified and the results are verified through simulations based on the evaluation model. Next, two global sensitivity indices are proposed and a sensitivity analysis is conducted using the Monte Carlo method throughout the reachable workspace of the manipulator. The results of the sensitivity analysis indicate that 10 geometric errors have no effects on the average angular comprehensive deformation and that the identified geometric errors have greater effects on the average linear comprehensive deformation. Therefore, the distribution of the global sensitivity index of the average linear comprehensive deformation is more meaningful for accuracy synthesis. Finally, simulations are performed to verify the results of sensitivity analysis. Full article

Intelligent fault diagnosis for a single wind turbine is hindered by the lack of sufficient useful data, while multi-turbines have various faults, resulting in complex distributions. Collaborative intelligence can better solve these problems. Therefore, a peer-to-peer network is constructed with one node corresponding to one wind turbine in a cluster. Each node is equivalent and functional replicable with a new federated transfer learning method, including model transfer based on multi-task learning and model fusion based on dynamic adaptive weight adjustment. Models with convolutional neural networks are trained locally and transmitted among the nodes. A solution for the processes of data management, information transmission, model transfer and fusion is provided. Experiments are conducted on a fault signal testing bed and bearing dataset of Case Western Reserve University. The results show the excellent performance of the method for fault diagnosis of a gearbox in a wind turbine cluster. Full article

In this paper, a unique overview of intelligent machines and mathematical methods designed and developed to measure and to control the water level in industrial or laboratory setups of coupled and cascaded configurations of tanks is made. A systematized and concise overview is made of the mechatronic systems used in the measurement, identification, and control of the water level enumerates, the software used in the associated scientific research, modern techniques and sensors, and mathematical models, as well as analysis and control strategies. The broad overview of applications of the last decade is finalized by a proposition of a control system that is based on a parameter estimation of a new experimental setup, an integral dynamic model of the system, a modern mechatronic machine such as the Watson-Marlow peristaltic pump, the Anderson Negele sensor of level, the NI cRIO-9074 controller, and LabVIEW virtual instrumentation. The results of real experimental tests, exploiting a hybrid proportional control, being improved by a numerically predicted water level, are obtained using a few tools, i.e., the static characteristics, the classical step response, and a new pyramid-shaped step function of a discontinuous path-following reference input, being introduced to evaluate the effectiveness and robustness of the regulation of the level height. Full article

Additively manufactured metallic parts usually need postprocessing in order to achieve required shape accuracy. Cylindrical test specimens were produced by selective laser melting from Ti6Al4V powder material with different processing parameters. The aim of postprocessing was modification of shape accuracy. Sliding friction diamond burnishing was applied as the postprocessing method. A five-factor, two-level full factorial design of experiment was implemented with factors being infill laser power, infill laser scan speed, burnishing speed, feed and force. Improvement ratios of two roundness parameters were defined, calculated from experimental data, and studied by main effect and interaction analysis. It has been demonstrated that burnishing feed has the largest main effect to improvement in roundness total and cylindricity. Additionally, parameters of both selective laser melting and diamond burnishing appear in three largest interaction terms. Empirical functions were fit to measurement data. Results show that improvement in roundness parameters are strongly nonlinear functions of all factors. Full article

Universities and other educational institutions may find it difficult to afford the cost of obtaining cutting-edge teaching resources. This study introduces the adoption of a novel open prototyping framework in the context of mechatronics education, employing low-cost commercial off-the-shelf (COTS) components and tools for the motion control module. The goal of this study is to propose a novel structure for the motion control module in the engineering mechatronics curriculum. The objective is to foster a new teaching method. From a methodology perspective, students are involved in a series of well-organised theoretical lectures as well as practical, very engaging group projects in the lab. To help students understand, draw connections, and broaden their knowledge, the methods of surface learning and deep learning are frequently mixed thoroughly. The structure of the course as well as the key topics are discussed. The proposed open framework, which consists of an elevator model, is presented in details. Students’ early evaluation indicates that the course organisation and subjects are successful and beneficial. Full article

Compared with traditional machine tools, the micro machine tools have advantages of small volume, low cost, less energy consumption, high efficiency and high flexibility. Therefore, it is regarded as an important equipment for micro-cutting machining which has been used widely all over the world and. As a key component of the micro-cutting machine tools, the body structure directly influences the machining performance. Thus, an integral column and base structure for micro machining tools was proposed in this work, and the detailed structural parameters were designed based on parameter analysis. Besides, the static and dynamic performance of the proposed machine were analyzed and compared between the integral structure and the separated one. The deformation and stress of the proposed structures under typical working conditions were studied by numerical simulation, along with the natural frequencies, vibration modes and frequency response peaks. Further, optimization was performed on the integral body structure, the prototype of the micro-machine tool was trial-produced, and the positioning accuracy of each coordinate axis was qualitatively analyzed. In addition, the micro-milling test was carried out with 6061 aluminum alloy to show the performance of the novel cutting machine. The results revealed that the proposed integrated micro-machine bed structure is superior to the separated structure in terms of static deformation and harmonic response characteristics, with good comprehensive mechanical properties, greatly improved static and dynamic performance of the machine tool, significantly improved structural accuracy, improved processing quality of the specimen and good application value. Full article

The development of morphing wings is in the pursuit of lighter weight, higher stiffness and strength, and better flexible morphing ability. A structure that can be used as both the bearing structure and the morphing mechanism is the optimal choice for the morphing wing. A morphing wing composed of a tensegrity structure and a non-overconstrained parallel mechanism was designed. The self-balancing trigonal bipyramidal tensegrity structure was designed based on the shape-finding method and force-equilibrium equation of nodes. The 4SPS-RS parallel mechanism that can complete wing morphing was designed based on the configuration synthesis method. The degree of freedom and inverse solution of the parallel mechanism was obtained based on the screw theory, and the Jacobian matrix of the parallel mechanism was established. The stiffness model of the tensegrity structure and the 4SPS-RS parallel mechanism was established. The relationship between the deformation of the 4SPS-RS parallel mechanism and sweep angle, torsion angle, spanwise bending, and span was obtained. Through the modular assembly and distributed drive, the morphing wing could perform smooth and continuous morphing locally and globally. In the static state, it has the advantages of high stiffness and large bearing capacity. In the process of morphing, it can complete morphing motion with four degrees of freedom in changing sweep, twist, spanwise bending, and span of the wing. Full article

The research into the tracking methods of unmanned aerial vehicles (UAVs) for agile targets is multi-disciplinary, with important application scenarios. Using a quadrotor as an example, in this paper, we mainly researched the tracking-related modeling and application verification of agile targets. We propose a robust and efficient UAV path planning approach for tracking agile targets aggressively and safely. This approach comprehensively takes into account the historical observations of the tracking target and the surrounding environment of the location. It reliably predicts a short time horizon position of the moving target with respect to the dynamic constraints. Firstly, via leveraging the Bernstein basis polynomial and combining obstacle distribution information around the target, the prediction module evaluated the future movement of the target, presuming that it endeavored to stay away from the obstacles. Then, a target-informed dynamic searching method was embraced as the front end, which heuristically searched for a safe tracking trajectory. Secondly, the back-end optimizer ameliorated it into a spatial–temporal optimal and collision-free trajectory. Finally, the tracking trajectory planner generated smooth, dynamically feasible, and collision-free polynomial trajectories in milliseconds, which is consequently reasonable for online target tracking with a restricted detecting range. Statistical analysis, simulation, and benchmark comparisons show that the proposed method has at least 40% superior accuracy compared to the leading methods in the field and advanced capabilities for tracking agile targets. Full article

This paper deals with the design and optimization of a 2.1 MW rim-driven electric thruster for ship propulsion. For this purpose, a double stator ironless rotor axial flux permanent magnet (AFPM) motor is considered as the propulsion motor. The analytical model of the selected AFPM motor is presented. The magnetic field in the AFPM machine is calculated using the 3D magnetic charge concept in combination with image theory and permeance functions to take into account the stator slotting effects, and a simple thermal model is used to evaluate the heat dissipation capabilities of the machine and the thermal dependence of the main electromagnetic losses. To optimally design the AFPM, an optimization process based on genetic algorithms is applied to minimize the cost of the active motor materials. An appropriate objective function has been constructed, and different constraints related to the main electrical, geometrical, and mechanical parameters have been taken into account. The achieved results are compared with the performance of a podded radial flux permanent magnet (RFPM) motor, which is considered a reference propulsion motor. The obtained results show a fairly satisfactory improvement in the cost and masses of the active motor materials. Finally, the accuracy of the obtained optimum solution is validated by performing 3D finite element analysis (3D-FEA) simulations. Full article

This paper proposes a full-size and free-flying humanoid robot named Taikobot that aims to assist astronauts in a space station and maintain spacecrafts between human visits. Taikobot adopts a compact and lightweight (∼25 kg) design to work in microgravity, which also reduces launch costs and improves safety during human–robot collaboration. Taikobot’s anthropomorphic dual arm system and zero-g legs allow it to share a set of intravehicular human–machine interfaces. Unlike ground-walking robots, Taikobot maneuvers in a novel push–flight–park (PFP) strategy as an equivalent astronaut in a space station to maximize workspace and flexibility. We propose a PFP motion planning and control method based on centroidal dynamics and multi-contact model. Based on the proposed method, we carried out extensive simulations and verified the feasibility and advantages of the novel locomotion strategy. We also developed a prototype of Taikobot and carried out several ground experiments on typical scenarios where the robot collaborates with human astronauts. The experiments show that Taikobot can do some simple and repetitive tasks along with astronauts and has the potential to help astronauts improve their onboard working efficiency. Full article

Lower limb rehabilitation robot (LLRR) can effectively help restore the lower limb’s motor function of patients with hemiplegia caused by stroke through a large number of targeted and repetitive rehabilitation training. To improve the safety and comfort of robot-assisted lower limb rehabilitation, we developed an LLRR with rigid-flexible characteristics; the design of passive joints is used to improve human-machine compatibility; the design of flexible unit makes the mechanism have certain rigid-flexible characteristics. Three different rehabilitation training methods have been developed to adapt to the patients at different stages of rehabilitation, namely, passive exercise, active exercise and resistance exercise, respectively. Experiments with healthy subjects have been conducted to verify the effectiveness of the development of the different training modes of the LLRR, showing good compatibility of the mechanism and good trajectory tracking performance of the developed training methods. Full article

Titanium alloys are widely used in aerospace, the military industry, electronics, automotive fields, etc., due to their excellent properties such as low density, high strength, high-temperature resistance, and corrosion resistance. Many components need to be finished precisely after being cut in these applications. In order to achieve high-quality and high-efficiency finishing of titanium alloys, ultrasonic-assisted magnetic abrasive finishing (UAMAF) was introduced in this research. The machining performance for UAMAF of a titanium alloy was studied by experimentally comparing UAMAF and magnetic abrasive finishing (MAF). The results show that the cutting force of UAMAF can reach 2 to 4 times that of MAF, and it decreases rapidly with the increase in the machining gap due to the energy loss of ultrasonic impact in the transmission between magnetic abrasives. The surface roughness of UAMAF can reach about Ra 0.075 μm, which is reduced by about 59% compared with MAF. The main wear type of the magnetic abrasive is that the diamond grits fell off the magnetic abrasive in both UAMAF and MAF. The uniform wear of the magnetic abrasive is realized, and the utilization ratio of the magnetic abrasive is obviously improved in UAMAF. Full article

This article proposes a two-stage simultaneous localization and mapping (SLAM) method based on using the red green blue-depth (RGB-D) camera in dynamic environments, which can not only improve tracking robustness and trajectory accuracy but also reconstruct a clean and dense static background model in dynamic environments. In the first stage, to accurately exclude the interference of features in the dynamic region from the tracking, the dynamic object mask is extracted by Mask-RCNN and optimized by using the connected component analysis method and a reference frame-based method. Then, the feature points, lines, and planes in the nondynamic object area are used to construct an optimization model to improve the tracking accuracy and robustness. After the tracking is completed, the mask is further optimized by the multiview projection method. In the second stage, to accurately obtain the pending area, which contains the dynamic object area and the newly added area in each frame, a method is proposed, which is based on a ray-casting algorithm and fully uses the result of the first stage. To extract the static region from the pending region, this paper designs divisible and indivisible regions process methods and the bounding box tracking method. Then, the extracted static regions are merged into the map using the truncated signed distance function method. Finally, the clean static background model is obtained. Our methods have been verified on public datasets and real scenes. The results show that the presented methods achieve comparable or better trajectory accuracy and the best robustness, and can construct a clean static background model in a dynamic scene. Full article

The higher the natural frequency of the material is, the more resistant it is to deformation under impulse loading. To explore the influence of micro-abrasive and micro-jet impact on the natural frequency and resonance amplitude value of the material under ultrasonic cavitation, 18 sets of single-factor controlled variable ultrasonic cavitation experiments were carried out on a polished specimen of 6061 aluminum alloy (30 mm × 30 mm × 10 mm). With the increase of the abrasive content in the suspension, the natural frequency of the workpiece first increased, then decreased and remained stable. With the increase of the ultrasonic amplitude, the resonance amplitude value of the material increased, reaching the maximum at 0.1789 m·s⁻² and then decreased. The effect of ultrasonic amplitude on the natural frequency of the material was greater than that of the abrasive content, and the effect of the abrasive content on the common amplitude value was greater than that of the ultrasonic amplitude. This research provides a certain reference significance for exploring the influence of power ultrasonic micro-cutting on material properties and avoiding the occurrence of resonance phenomenon of the workpiece under different working conditions. Full article

Under variable working conditions, the tool status signal is affected by changing machine processing parameters, resulting in a decreased prediction accuracy of the remaining useful life (RUL). Aiming at this problem, a method based on multi-sensor fusion for tool RUL prediction was proposed. Firstly, the factorization machine (FM) was used to extract the nonlinear processing features in the low-frequency condition signal, and the one-dimensional separable convolution was applied to extract tool life state features from multi-channel high-frequency sensor signals. Secondly, the residual attention mechanism was introduced to weight the low-frequency condition characteristics and high-frequency state characteristics, respectively. Finally, the features extracted in the low-frequency and high-frequency parts were input into the full connection layer to integrate working condition information and state information to suppress the influence of variable conditions and improve prediction accuracy. The experimental results demonstrated that the method could predict the remaining life of the tool effectively, and the accuracy and stability of the model are better than several other methods. Full article

Speed variable electric drives play a key role in the evolution of electrical mobility. The dynamic performance of these systems is a crucial feature for security purposes. For this reason, a large number of works are focused on identification of the most appropriate control technique to satisfy a transient scenario. In this regard, the dynamic abilities of linear and direct controllers were analysed for three-phase drives. Although some insights about their transient performance were obtained, there are yet some issues to be solved. For instance, speed response was typically omitted, some influencing factors were neglected or the multiphase case was carried out. Considering this information, this work proposes a comparative analysis of the dynamic performance of the most popular regulation strategies for a six-phase electric drive. This study analyses speed, current and voltage responses to achieve an overall view of the system performance. Two concepts were employed to simplify the comprehension of the dynamic behavior of a regulation strategy: reaction time and response capacity. Experimental results are employed to confirm the impact of the different agents on a transient situation. Full article

Current state-of-the-art engine condition monitoring is based on a minimum of one steady-state data point per flight. Due to the scarcity of available data points, there are difficulties distinguishing between random scatter and an underlying fault introducing a detection latency of several flights. Today’s increased availability of data acquisition hardware in modern aircraft provides continuously sampled in-flight measurements, so-called full-flight data. These full-flight data give access to sufficient data points to detect faults within a single flight, significantly improving the availability and safety of aircraft. Artificial neural networks are considered well suited for the timely analysis of an extensive amount of incoming data. This article proposes uncertainty quantification for artificial neural networks, leading to more reliable and robust fault detection. An existing approach for approximating the aleatoric uncertainty was extended by an Out-of-Distribution Detection in order to take the epistemic uncertainty into account. The method was statistically evaluated, and a grid search was performed to evaluate optimal parameter combinations maximizing the true positive detection rates. All test cases were derived based on in-flight measurements of a commercially operated regional jet. Especially when requiring low false positive detection rates, the true positive detections could be improved 2.8 times while improving response times by approximately 6.9 compared to methods only accounting for the aleatoric uncertainty. Full article

Degradation of the ignition system can result in startup failure in an aircraft’s auxiliary power unit. In this paper, a novel acoustics-based solution that can enable condition monitoring of an APU ignition system was proposed. In order to support the implementation of this research study, the experimental data set from Cranfield University’s Boeing 737-400 aircraft was utilized. The overall execution of the approach comprised background noise suppression, estimation of the spark repetition frequency and its fluctuation, spark event segmentation, and feature extraction, in order to monitor the state of the ignition system. The methodology successfully demonstrated the usefulness of the approach in terms of detecting inconsistencies in the behavior of the ignition exciter, as well as detecting trends in the degradation of spark acoustic characteristics. The identified features proved to be robust against non-stationary background noise, and were also found to be independent of the acoustic path between the igniter and microphone locations, qualifying an acoustics-based approach to be practically viable. Full article

Hydrostatic bearing spindles are widely applied in high precision grinding and turning machines due to their good dynamic stability and rotational accuracy. However, under the condition of high-speed rotations, the heat generated by the friction of the oil film will cause the shear thinning effect. It not only reduces the rotation accuracy of the spindle but also reduces the service life of the spindle. The surface texture structure and configuration between the planes play the role of homogenizing oil film temperature and preventing the bearing surface wear caused by excessive concentration of temperature, which can change the relative motion from the inside of the oil film and thus improve the performance of the hydrostatic spindle more effectively. In this paper, the influence of the surface texture shape and height on the thrust bearing performance of the hydrostatic spindle is systematically investigated by comparative analysis. The CFD simulations are developed to analyze the computational results based on the theory of viscosity-temperature characteristics. The results show that when the height of the surface structure is 1 ~ 2 times the oil film thickness, the spindle bearing performance is the best. The average temperature in the bearing region is the lowest and the accuracy of the simulations was verified by experimental results. Full article

Grinding processes’ stochastic nature poses a challenge in predicting the quality of the resulting surfaces. Post-production measurements for form, surface roughness, and circumferential waviness are commonly performed due to infeasibility in measuring all quality parameters during the grinding operation. Therefore, it is challenging to diagnose the root cause of quality deviations in real-time resulting from variations in the machine’s operating condition. This paper introduces a novel approach to predict the overall quality of the individual parts. The grinder is equipped with sensors to implement condition-based maintenance and is induced with five frequently occurring failure conditions for the experimental test runs. The crucial quality parameters are measured for the produced parts. Fuzzy c-means $\left(FCM\right)$ and Hotelling’s T-squared $\left(T^2\right)$ have been evaluated to generate quality labels from the multi-variate quality data. Benchmarked random forest regression models are trained using fault diagnosis feature set and quality labels. Quality labels from the $T^2$ statistic of quality parameters are preferred over $FCM$ approach for their repeatability. The model, trained from $T^2$ labels achieves more than $94\%$ accuracy when compared to the measured ring disposition. The predicted overall quality using the sensors’ feature set is compared against the threshold to reach a trustworthy maintenance decision. Full article

Vascular interventional robots have attracted growing attention in recent years. However, current vascular interventional robot systems generally lack force feedback and cannot quickly clamp the catheter/guidewire. The structure of slave systems is unstable and the power transmission is imprecise, increasing the system’s safety hazards. Vascular intervention robots generally do not follow traditional surgeons’ operation habits and, thus, it is not easy for them to understand and learn how to operate. Therefore, a novel vascular intervention system is proposed. The slave system can quickly clamp the catheter/guidewire, is compatible with various standard catheter/guidewire sizes, has precise power transmission, and has a stable structure. The surface of the catheter/guidewire is clamped without damage. Whether it is on the master side or the slave side, it follows the habits of traditional operators to a great extent. The results show that the measurement accuracy of the axial force meets the requirements of robot-assisted surgery and the system can track the designed position of the catheter/guidewire in real time. This study makes a certain contribution to the development of master–slave systems for endovascular tele-surgery. Full article

In this study, a robotic fish inspired to carangiform swimmers has been developed. The artifact presents a new transmission system that employs the magnetic field interaction of permanent magnets to ensure waterproofness and prevention from any overload for the structure and the actuating motor. This mechanism converts the rotary motion of the motor into oscillatory motion. Such an oscillating system, along with the wire-driven mechanism of the tail, generates the required traveling wave in the robotic fish. The complete free swimming robotic fish, measuring $179\mathrm{mm}$ in length with a mass of only $77\mathrm{g}$, was able to maintain correct posture and neutral buoyancy in water. Multiple experiments were conducted to test the robotic fish performance. It could swim with a maximal speed of $0.73$ body lengths per second ($0.13\mathrm{m}/\mathrm{s}$) at a tail beat frequency of $3.25\mathrm{Hz}$ and an electric power consumption of $0.67\mathrm{W}$. Furthermore, the robotic fish touched the upper bound of the efficient swimming range, expressed by the dimensionless Strouhal number: $0.43$ at $1.75\mathrm{Hz}$ tail beat frequency. The lowest energy to travel 1 meter was $4.73$ Joules for the final prototype. Future works will focus on endowing the robot with energy and navigation autonomy, and on testing its potential for real-world applications such as environmental monitoring and animal–robot interaction. Full article

The rise of the fourth industrial revolution aspires to digitize any traditional manufacturing process, paving the way for new organisation schemes and management principles that affect business models, the environment, and services across the entire value chain. During the last two decades, the generated advancements have been analysed and discussed from a bunch of technological and business perspectives gleaned from a variety of academic journals. With the aim to identify the digital footprint of Industry 4.0 in the current manufacturing ecosystem, a systematic literature survey of surveys is conducted here, based on survey academic articles that cover the current state-of-the-art. The 59 selected high-impact survey manuscripts are analysed using PRISMA principles and categorized according to their technologies under analysis and impact, providing valuable insights for the research and business community. Specifically, the influence Industry 4.0 exerts on traditional business models, small and medium-sized enterprises, decision-making processes, human–machine interaction, and circularity affairs are investigated and brought out, while research gaps, business opportunities, and their relevance to Industry 5.0 principles are pointed out. Full article

Investigations about the feasibility of delivery systems with unmanned aerial vehicles (UAVs) or drones have been recently expanded, owing to the exponential demand for goods to be delivered in the recent years, which has been further increased by the COVID-19 pandemic. UAV delivery can provide new contactless delivery strategies, in addition to applications for medical items, such as blood, medicines, or vaccines. The safe delivery of goods is paramount for such applications, which is facilitated if the payload is embedded in the main drone body. In this paper, we investigate payload solutions for medium and small package delivery (up to 5 kg) with a medium-sized UAV (maximum takeoff of less than 25 kg), focusing on (i) embedded solutions (packaging hosted in the drone fuselage), (ii) compatibility with transportation of medical items, and (iii) user-oriented design (usability and safety). We evaluate the design process for possible payload solutions, from an analysis of the package design (material selection, shape definition, and product industrialization) to package integration with the drone fuselage (possible solutions and comparison of quick-release systems). We present a prototype for an industrialized package, a right prism with an octagonal section made of high-performance double-wall cardboard, and introduce a set of concepts for a quick-release system, which are compared with a set of six functional parameters (mass, realization, accessibility, locking, protection, and resistance). Further analyses are already ongoing, with the aim of integrating monitoring and control capabilities into the package design to assess the condition of the delivered goods during transportation. Full article

The study sets a baseline for split DC link capacitance values and voltage set points in three-phase three-level AC/DC (or DC/AC) converters operating with unity power factor. In order to equalize the average values of partial DC link voltages, the controller generates a zero-sequence containing DC components only while employing neither dedicated DC link capacitance balancing hardware nor high-order zero-sequence component injection. Such a baseline is required in order to evaluate the effectiveness of different DC link capacitance reduction methods proposed in the literature. Unlike most previous works, utilizing neutral point current based on cumbersome analytical expressions to determine neutral point potential oscillations, the instantaneous power balance-based approach is employed in this paper, resulting in greatly simplified and more intuitive expressions. It is demonstrated that while the total DC link voltage is low-frequency ripple-free under unity power factor balanced AC-side operation, split DC link capacitors absorb triple-fundamental frequency power components with one-sixth load power magnitude. This yields significant opposite phase partial voltage ripples. In such a case, selection of DC link capacitances and voltage set points must take into account the expected values of AC-side phase voltage magnitude and split DC link capacitor voltage and current ratings. Simulation and experimental results validate the proposed methodology by application to a 10 kVA T-type converter prototype. Full article

Yōkobo is a robject; it was designed using the principle of slow technology and it aims to strengthen the bond between members (e.g., a couple). It greets people at the entrance and mirrors their interactions and the environment around them. It was constructed by applying the notions of a human–robot–human interaction. Created by joint work between designers and engineers, the form factor (semi-abstract) and the behaviours (nonverbal) were iteratively formed from the early stage of the design process. Integrated into the smart home, Yōkobo uses expressive motion as a communication medium. Yōkobo was tested in our office to evaluate its technical robustness and motion perception ahead of future long-term experiments with the target population. The results show that Yōkobo can sustain long-term interaction and serve as a welcoming partner. Full article

The concept of a digital twin is increasingly appearing in industrial applications, including the field of predictive maintenance. A digital twin is a virtual representation of a physical system containing all data available on site. This paper presents condition monitoring of ventilation systems through the digital twin approach. A literature review regarding the most popular system faults is covered. The motor current signature analysis is used in this research to detect system faults. The physical system is further described. Then, based on the free body diagram concept and Newton’s second law, the equations of motion are obtained. Matlab/Simulink software is used to build the digital twin. The Concordia method and the Fast Fourier Transform analysis are used to process the current signal, and physical and numerical system current measurements are obtained and compared. In the final step of the modeling, specific frequencies were adjusted in the twin to achieve the best simulation. In addition, a statistical approach is used to create a complete diagnostic protocol. Full article

With the rapid development of artificial intelligence (AI) technology and an increasing demand for redundant robotic systems, robot control systems are becoming increasingly complex. Although forward kinematics (FK) and inverse kinematics (IK) equations have been used as basic and perfect solutions for robot posture control, both equations have a significant drawback. When a robotic system is highly nonlinear, it is difficult or impossible to derive both the equations. In this paper, we propose a new method that can replace both the FK and IK equations of a seven-degrees-of-freedom (7-DOF) robot manipulator. This method is based on reinforcement learning (RL) and artificial neural networks (ANN) for supervised learning (SL). RL was used to acquire training datasets consisting of six posture data in Cartesian space and seven motor angle data in joint space. The ANN is used to make the discrete training data continuous, which implies that the trained ANN infers any new data. Qualitative and quantitative evaluations of the proposed method were performed through computer simulation. The results show that the proposed method is sufficient to control the robot manipulator as efficiently as the IK equation. Full article

The paper discusses the state-of-the-art of locomotion systems for ground mobile robots comprising tracks. Tracked locomotion, due to the large contact surface with the ground, is particularly suitable for tackling soft, yielding, and irregular terrains, but is characterized by lower speed and energy efficiency than wheeled locomotion, and lower obstacle-climbing capability than legged locomotion. Therefore, in recent years academic and industrial researchers have designed a wide variety of hybrid solutions, combining tracks with legs and wheels. The paper proposes three possible parallel taxonomies, based on body architecture, track profile, and track type, to help designers select the most suitable architecture on the basis of the operative necessities. Moreover, modeling, simulation, and design methodologies for tracked ground mobile robots are recalled. Full article

Robots have been critical instruments to exploration of extreme environments by providing access to environments beyond human limitations. Jumping robot concepts are attractive solutions to negotiate complex and cluttered terrain. However, among the engineering challenges that need to be addressed to enable sustained operation of jumping robot concepts in extreme environments, the reduction of mechanical failure modes is one of the most fundamental. This study sets out to develop a jumping robot design, with a focus on a minimal actuation to support reduced mechanism maintenance and thus limit the number of mechanical failure modes. We present the synthesis of a Sarrus-style linkage to constrain the system to a single translational degree of freedom thus removing the need for synchronising gears, which exhibit high failure rates in dusty environments. We have restricted the present research to vertical solid jumps to assess the performance of the fundamental main-drive linkage. A laboratory demonstrator assists the transfer of theoretical concepts and approaches to practical implementation. The laboratory demonstrator performs jumps with 63% potential-to-kinetic energy conversion efficiency, with a theoretical maximum of 73%. Satisfactory operation opens up design optimisation and directional jump capability towards the development of a jumping robotic platform for extreme environments exploration. Full article

This paper presents an analysis of the scientific literature devoted to the problem of load sharing and phasing in planetary gearboxes. The wide range of research topics demonstrates the technical challenges of understanding planetary load-sharing and planet phasing. This review includes studies having the goal of developing models for load sharing and exploring the positive or negative effects of different parameters such as phasing on the load distribution among planets. Practical aspects are also considered, for example, the effects of some errors that are unavoidable during manufacturing or working conditions, e.g., misalignments or position errors. Methods for improving the load-sharing characteristics, e.g., flexible ring or floating components, are discussed as well. Full article

The state of the art of high-torque electric motors has been reviewed in this paper. This paper presents a literature review of high-torque density electric machines based on their airgap classifications, which brings a unique consideration to new design ideas to increase torque density. Electric machines are classified into three main groups based on their airgap configuration, i.e., (1) machines with a constant airgap, (2) machines with a variable airgap, and (3) machines with an eccentric airgap. This paper also presents the modeling of a high-torque airgap-less electric motor based on the concept of eccentric airgap. The torque density of this motor has been compared to motors available in the literature review. Among electrical motors with no permanent-magnet, airgap-less electric motors take the lead in terms of torque density, which is almost five times greater than the next motor, “in-wheel for electric vehicle”. Full article

Recently, numerous new meta-heuristic algorithms have been proposed for solving optimization problems. According to the Non-Free Lunch theorem, we learn that no single algorithm can solve all optimization problems. In order to solve industrial engineering design problems more efficiently, we, inspired by the algorithm framework of the Arithmetic Optimization Algorithm (AOA) and the Harris Hawks Optimization (HHO), propose a novel hybrid algorithm based on these two algorithms, named EAOAHHO in this paper. The pinhole imaging opposition-based learning is introduced into the proposed algorithm to increase the original population diversity and the capability to escape from local optima. Furthermore, the introduction of composite mutation strategy enhances the proposed EAOAHHO exploitation and exploration to obtain better convergence accuracy. The performance of EAOAHHO is verified on 23 benchmark functions and the IEEE CEC2017 test suite. Finally, we verify the superiority of the proposed EAOAHHO over the other advanced meta-heuristic algorithms for solving four industrial engineering design problems. Full article

The erratic modern world introduces challenges to all sectors of societies and potentially introduces additional inequality. One possibility to decrease the educational inequality is to provide remote access to facilities that enable learning and training. A similar approach of remote resource usage can be utilized in resource-poor situations where the required equipment is available at other premises. The concept of Industry 5.0 (i5.0) focuses on a human-centric approach, enabling technologies to concentrate on human–machine interaction and emphasizing the importance of societal values. This paper introduces a novel robotics teleoperation platform supported by the i5.0. The platform reduces inequality and allows usage and learning of robotics remotely independently of time and location. The platform is based on digital twins with bi-directional data transmission between the physical and digital counterparts. The proposed system allows teleoperation, remote programming, and near real-time monitoring of controlled robots, robot time scheduling, and social interaction between users. The system design and implementation are described in detail, followed by experimental results. Full article

Stroke causes disability in the lower-limb symmetry gait pattern in affected patients. The patients would not be able to regain their usual walking ability independently unless they benefit from rehabilitation therapies. Footplate-based gait robo-assisted systems can help patients to conduct effective training/exercising while tracking their progress of recovery and can dramatically reduce the clinical labor costs of physiotherapy. In the sense of simulation and not the design of the mechanical structure, this study aims to perform a combination of dynamic and control simulation of a five degrees-of-freedom footplate-based gait robo-assisted system established according to the Stewart platform structure for use in lower limb rehabilitation of stroke patients. The effectiveness and performance of the proposed mechanism were assessed through a clinical gait pattern of a healthy male individual. The proposed robo-assisted system enables the simulation of the hip and knee flexion/extension as well as the ankle dorsiflexion/plantar flexion of stroke patients to reproduce their typical symmetry gait pattern. The results were interpreted as the dynamic movement characteristics of the right and left thigh, leg, and foot compared to the clinical gait pattern with a mean percentage error of 6.70% to show the effectiveness and accuracy of the developed robo-assisted system for lower limb actuation in the simulation process. Full article

To advance the active safety performance for vehicles, especially in extreme conditions, an active rear steering (ARS) control system is designed in this paper. A driver model is established to simulate the driving behaviour of a human driver who is in charge of the front steering control. In the ARS control system, the sliding mode predictive control (SMPC) approach is applied to the ARS controller design based on a 3 degrees of freedom (DoF) nonlinear vehicle model. In the ARS controller design, four kinds of active safety performances are considered, namely, path-tracking performance, handling performance, lateral stability, and rollover prevention. Furthermore, the priority of the four kinds of active safety performance is defined. According to the control priority, an event-triggered mechanism (ETM) is designed to adjust the SMPC controller of the ARS system to address different driving conditions. Finally, two simulation cases are conducted to evaluate the performance of the proposed ARS system. The results show that the ARS system is in favour of the active safety performance advancement for human drivers. Additionally, the comparative simulation indicates that the SMPC algorithm is superior to the fast terminal sliding mode control (FTSMC) algorithm. Full article

Investigation into non-destructive testing and evaluation of 3D printing quality is relevant due to the lack of reliable methods for non-destructive testing of 3D printing defects, including testing of the surface quality of 3D printed parts. The article shows how it is possible to increase the efficiency of online monitoring of the quality of the 3D printing technological process through the use of an optical contactless high-performance measuring instrument. A comparative study of contact (R130 roughness tester) and non-contact (LJ-8020 laser profiler) methods for determining the height of irregularities on the surface of a steel reference specimen was performed. It was found that, in the range of operation of the contact method (Ra 0.03–6.3 µm and Rz 0.2–18.5 µm), the errors of the contactless method in determining the standard surface roughness indicators Ra and Rz were 23.7% and 1.6%, respectively. Similar comparative studies of contact and non-contact methods were performed with three defect-free samples made of plastic polylactic acid (PLA), with surface irregularities within the specified range of operation of the contact method. The corresponding errors increased and amounted to 65.96% and 76.32%. Finally, investigations were carried out using only the non-contact method for samples with different types of 3D printing defects. It was found that the following power spectral density (PSD) estimates can be used as diagnostic features for determining 3D printing defects: Variance and Median. These generalized estimates are the most sensitive to 3D printing defects and can be used as diagnostic features in online monitoring of object surface quality in 3D printing. Full article