Editor’s Choice Articles

Voluntary hand movements are usually impaired after a cerebral stroke, affecting millions of people per year worldwide. Recently, the use of hand exoskeletons for assistance and motor rehabilitation has become increasingly widespread. This study presents a novel hand exoskeleton, designed to be low cost, wearable, easily adaptable and suitable for home use. Most of the components of the exoskeleton are 3D printed, allowing for easy replication, customization and maintenance at a low cost. A strongly underactuated mechanical system allows one to synergically move the four fingers by means of a single actuator through a rigid transmission, while the thumb is kept in an adduction or abduction position. The exoskeleton’s ability to extend a typical hypertonic paretic hand of stroke patients was firstly tested using the SimScape Multibody simulation environment; this helped in the choice of a proper electric actuator. Force-myography was used instead of the standard electromyography to voluntarily control the exoskeleton with more simplicity. The user can activate the flexion/extension of the exoskeleton by a weak contraction of two antagonist muscles. A symmetrical master–slave motion strategy (i.e., the paretic hand motion is activated by the healthy hand) is also available for patients with severe muscle atrophy. An inexpensive microcontroller board was used to implement the electronic control of the exoskeleton and provide feedback to the user. The entire exoskeleton including batteries can be worn on the patient’s arm. The ability to provide a fluid and safe grip, like that of a healthy hand, was verified through kinematic analyses obtained by processing high-framerate videos. The trajectories described by the phalanges of the natural and the exoskeleton finger were compared by means of cross-correlation coefficients; a similarity of about 80% was found. The time required for both closing and opening of the hand exoskeleton was about 0.9 s. A rigid cylindric handlebar containing a load cell measured an average power grasp force of 94.61 N, enough to assist the user in performing most of the activities of daily living. The exoskeleton can be used as an aid and to promote motor function recovery during patient’s neurorehabilitation therapy. Full article

Steering and suspension systems of a motor vehicle have very important mutual connections that have direct influence on a vehicle’s steerability, stability, comfort and life expectancy. These mechanical and functional couplings cause an intensive interaction between the two mentioned vehicle systems on a geometrical, kinematical and dynamical level. This article presents a study on nonparametric identification of dynamic interaction between the steering and the suspension system of a passenger vehicle. A specific methodology for experimental research in on-road conditions was designed that was in line with the research objectives and the applied measuring system. Experimental data were acquired for a curvilinear drive, with different constant driving speeds and on different roads. A multiple input/multiple output model for identification of the vehicle dynamics system from the aspect of interaction between the steering and the suspension system was developed. The analysis of experimental data was realized with the selection of a corresponding identification model, decoupling of model inputs and conditioned spectral analysis. The results of the conditioned spectral analysis of experimentally obtained data records indicate the level of interaction between the observed input and output parameters of the steering and the suspension systems to be in the frequency range below 30 Hz. Full article

Tilt bulldozing generates unbalanced loads on two push arms, which leads to the service lives of the two push arms being different. Because the push arms rotate in triaxial directions during tilt bulldozing, it is difficult to accurately analyze the fatigue life of the push arm with one specific boundary condition and loading history. Therefore, a fatigue analysis of the push arms under tilt bulldozing conditions is proposed based on co-simulation of RecurDyn-EDEM-AMESim in this paper. The control of tilt bulldozing conditions is realized automatically according to the tilt angle and blade depth. The dynamic loads of the push arms are accurately calculated in this virtual model. Subsequently, the stress–time histories are obtained to investigate the fatigue lives of push arms. Both the overall damage and the initiation positions of the cracks are predicted herein. It is determined that the fatigue lives of the right and left push arms are 7,317.84 h and 39,381.89 h, respectively. Thus, the life of the push arm on the blade’s tilted side is reduced by 81.42% compared to the other side. Additionally, experimental tests are conducted to verify the accuracy of the virtual model. Analysis results indicate that the strains of the push arms according to the virtual simulation are close to those measured in the experiments. Full article

Patient transfer has always been a difficult problem in the hospital. For medical staff, there are problems including high risk of infection, heavy physical labor and low efficiency of transfer; for patients, there are problems including poor comfort and secondary injury. In this paper, a novel bilateral patient transfer robot is investigated and designed. The following tasks are conducted: (1) Based on the process of patient transfer, a transfer model, which consists of two degrees of freedom, is proposed, and the working principle of bilateral patient transfer robot is obtained and analyzed in detail. (2) Force analysis of the patient transfer robot is conducted. The corresponding relationship between the patient comfort and the insertion angle is proposed, and the optimal sizes of mechanical structure are obtained. (3) Based on the theoretical analysis, the mechanical structure and the control system of the robot are designed, and the prototype is manufactured. (4) Experimental research is conducted. The results show that the prototype can complete the required motion performance with a carrying capacity up to 150 kg and patient comfort is excellent. The results of this paper prove that this kind of patient transfer robot has good performance, it can also reduce the burden on medical staff. Full article

Permanent magnet machines are widely applied in motor drive systems. Therefore, condition monitoring of permanent magnet machines has great significance to assist maintenance. High temperatures are accountable for lots of typical malfunctions and faults, such as demagnetization of the permanent magnet (PM) and inter-turn short circuit of stator windings. Therefore, temperature monitoring of the PM and stator windings is essential for reliable operation. In this paper, an overview introducing and evaluating existing thermal monitoring methods is presented. First, the mechanism of thermal-caused failures for the PM and stator windings is introduced. Then, the design procedure and principles of existing temperature monitoring methods are introduced and summarized. Next, the evaluations and recommendations of application feasibility are demonstrated. Finally, the potential future challenges and opportunities for temperature monitoring of the PM and stator windings are discussed. Full article

Modern vehicles rely on a multitude of sensors and cameras to both understand the environment around them and assist the driver in different situations. Lane detection is an overall process as it can be used in safety systems such as the lane departure warning system (LDWS). Lane detection may be used in steering assist systems, especially useful at night in the absence of light sources. Although developing such a system can be done simply by using global positioning system (GPS) maps, it is dependent on an internet connection or GPS signal, elements that may be absent in some locations. Because of this, such systems should also rely on computer vision algorithms. In this paper, we improve upon an existing lane detection method, by changing two distinct features, which in turn leads to better optimization and false lane marker rejection. We propose using a probabilistic Hough transform, instead of a regular one, as well as using a parallelogram region of interest (ROI), instead of a trapezoidal one. By using these two methods we obtain an increase in overall runtime of approximately 30%, as well as an increase in accuracy of up to 3%, compared to the original method. Full article

The grinding wheel topography influences the cutting performance and thus the economic efficiency of a grinding process. In contrary to conventional grinding wheels, super abrasive grinding wheels should undergo an additional sharpening process after the initial profiling process to obtain a suitable microstructure of the grinding wheel. Due to the lack of scientific knowledge, the sharpening process is mostly performed manually in industrial practice. A CNC-controlled sharpening process can not only improve the reproducibility of grinding processes but also decrease the secondary processing time and thereby increase the economic efficiency significantly. To optimize the sharpening process, experimental investigations were carried out to identify the significant sharpening parameters influencing the grinding wheel topography. The sharpening block width l_Sb, the grain size of the sharpening block d_kSb and the area-related material removal in sharpening V’’_Sb were identified as the most significant parameters. Additional experiments were performed to further quantify the influence of the significant sharpening parameters. Based on that, a process model was developed to predict the required sharpening parameters for certain target topographies. By using the process model, constant work results and improved process reliability can be obtained. Full article

There have been many reports of damage to wind turbine blades caused by lightning strikes in Japan. In some of these cases, the blades struck by lightning continue to rotate, causing more serious secondary damage. To prevent such accidents, it is a requirement that a lightning detection system is installed on the wind turbine in areas where winter lightning occurs in Japan. This immediately stops the wind turbine if the system detects a lightning strike. Normally, these wind turbines are restarted after confirming soundness of the blade through visual inspection. However, it is often difficult to confirm the soundness of the blade visually for reasons such as bad weather. This process prolongs the time taken to restart, and it is one of the causes that reduces the availability of the wind turbines. In this research, we constructed a damage detection model for wind turbine blades using machine learning based on SCADA system data and, thereby, considered whether the technology automatically confirms the soundness of wind turbine blades. Full article

This paper proposes a model-based two-degree-of-freedom (2DOF) speed control for a medium voltage (MV) variable speed drive (VSD) connected to a centrifugal compressor (CC) train. Torsional mode excitations in the drive shaft due to converter switching behaviour are considered. An effective description of the harmonics transfer is proposed. The tuning strategy aims to optimize the tracking behaviour of the step and ramp command, taking care of critical speed excitations. The stability of the closed-loop dynamics against time delay and drive parameter variations are studied by means of Nyquist diagrams and time-domain simulations. A descriptive method for the process damping behaviour is proposed. The control strategy is evaluated through simulations as well as an experimental setup, based on a hardware in the loop (HIL) in a master–slave configuration. Full article

In turning operations, the wear of cutting tools is inevitable. As workpieces produced with worn tools may fail to meet specifications, the machining industries focus on replacement policies that mitigate the risk of losses due to scrap. Several strategies, from empiric laws to more advanced statistical models, have been proposed in the literature. More recently, many monitoring systems based on Artificial Intelligence (AI) techniques have been developed. Due to the scope of different artificial intelligence approaches, having a holistic view of the state of the art on this subject is complex, in part due to a lack of recent comprehensive reviews. This literature review therefore presents 20 years of literature on this subject obtained following a Systematic Literature Review (SLR) methodology. This SLR aims to answer the following research question: “How is the AI used in the framework of monitoring/predicting the condition of tools in stable turning condition?” To answer this research question, the “Scopus” database was consulted in order to gather relevant publications published between 1 January 2000 and 1 January 2021. The systematic approach yielded 8426 articles among which 102 correspond to the inclusion and exclusion criteria which limit the application of AI to stable turning operation and online prediction. A bibliometric analysis performed on these articles highlighted the growing interest of this subject in the recent years. A more in-depth analysis of the articles is also presented, mainly focusing on six AI techniques that are highly represented in the literature: Artificial Neural Network (ANN), fuzzy logic, Support Vector Machine (SVM), Self-Organizing Map (SOM), Hidden Markov Model (HMM), and Convolutional Neural Network (CNN). For each technique, the trends in the inputs, pre-processing techniques, and outputs of the AI are presented. The trends highlight the early and continuous importance of ANN, and the emerging interest of CNN for tool condition monitoring. The lack of common benchmark database for evaluating models performance does not allow clear comparisons of technique performance. Full article

A switched reluctance motor has a very simple structure which becomes its key signature and leads to various advantages. However, because of its double saliency and switching principle, the motor is also known to have a relatively high torque ripple, and this hinders its use as a high-performance drive. In this paper, a method to reduce torque ripple while maintaining average torque is introduced. Two elements are used to achieve this, namely, a non-uniform air-gap on the rotor-pole face and one hole in each non-uniform region, which maintains the saturation level of the air-gap. This approach preserves the mechanical simplicity of the motor and is easy to implement. Simulations and experiments were performed to verify the effectiveness of the proposed design. Full article

Underactuated robotic grippers have the advantage of lower cost, simpler control, and higher safety over the fully actuated grippers. In this study, an underactuated robotic finger is presented. The design issues that should be considered for stable grasping are discussed in detail. This robotic finger is applied to design a two-fingered underactuated gripper. Firstly, a new three-DOF linkage-driven robotic finger that combines a five-bar mechanism and a double parallelogram is presented. This special architecture allows us to put all of the required actuators into the palm. By adding a torsion spring and a mechanical stopper at a passive joint, this underactuated finger mechanism can be used to perform parallel grasping, shape-adaptive grasping, and environmental contact-based grasp. Secondly, the dynamic model of this robotic finger is developed to investigate how to select an appropriate torsion spring. The dynamic simulation is performed with a multi-body dynamic simulator to verify our proposed approach. Moreover, static grasp models of both two-point and three-point contact grasps are investigated. Finally, different types of grasping modes are verified experimentally with a two-fingered underactuated robotic gripper. Full article

From an aerodynamic point of view, the electric turbocharger for the air supply of an automotive fuel cell faces difficult requirements: it must not only control the pressure level of the fuel cell, but it also has to operate with very high efficiency over a wide range. This paper explores features for the compressor and the turbine of an existing electric turbocharger, which are intended to meet the specific requirements of a fuel cell in an experimentally validated numerical study. Adjustable diffuser or nozzle vanes in the compressor and turbine achieve wider operating ranges but compromise efficiency, especially because of the necessary gaps between vanes and end walls. For the turbine, there are additional efficiency losses since the pivoting of the nozzle vanes leads to incidence and thus to flow separation at the leading edge of the nozzle vanes and the rotor blades. An increase in the mass flow and a slight efficiency improvement of the turbine with the low solidity nozzle vanes counteracts these losses. For the compressor, a reduction in the diffuser height and its influence over the operating range and power consumption yields an increase in surge margin as well as in maximum efficiency. Full article

With commercial application of 5G networks, many researchers have started paying attention to real-time control in 5G networks. This paper focuses on dual auto guided vehicles collaborative transport scenarios and designs a formation control system in current commercial 5G networks. Firstly, the structure of the 5G network researched in this paper is introduced. Then the round-trip time of 5G networks is measured and analyzed. The result shows that although the 5G round-trip time has randomness, it is mainly concentrated in 19 ± 3 ms, and the jitter mainly in 0 ± 3 ms. The Kalman filter is applied to estimate the transmission delay and experiment result shows the effectiveness of the estimation. Furthermore, the total delay including transmission delay and execution delay in control system is discussed. After establishing the AGV kinematic and formation model, complete control system based on compensation method is proposed. Finally, an experiment is carried out. Compared to the result without formation control, maximum distance error is reduced by 82.61% on average, while maximum angle error 45.91% on average. The result shows the effectiveness of the control system in formation maintaining in 5G network. Full article

Recently, the number of vehicles equipped with the Lane Keeping Assistance System (LKAS) is increasing. Therefore, safety evaluation to validate the LKAS has become more important. However, the actual vehicle test for safety evaluation has disadvantages such as the need for professional manpower, the use of expensive equipment, and environmental constraints. Therefore, we attempted to solve this problem using the dual cameras system with only inexpensive and accessible cameras. The optimal position of the dual cameras, image and focal length correction, and lane detection methods proposed in previous studies were used, and a theoretical equation for calculating the distance from the front wheel of the vehicle to the driving lane was proposed. For the actual vehicle testing, LKAS safety evaluation scenarios proposed in previous studies were used. According to the test results, the maximum error was 0.17 m, which indicated the reliability of the method because all errors in the tested scenarios exhibited similar trends and values. Therefore, through the use of the proposed theoretical equations in conjunction with inexpensive cameras, it is possible to reduce time, cost, and environmental problems in the development, vehicle application, and safety evaluation of LKAS components. Full article

Precision machining (e.g., fine grinding, polishing) induced residual stress is very small and often not constant across the wafer and it is difficult to be directly obtained by stress testing equipment or Stoney equation. The residual stress could be obtained theoretically based on the principle of superposition in which the entire wafer deformation is taken as the sum of all deformations induced by the residual stresses of different positions on the wafer surface. However, the solved residual stress is affected greatly by deformation measurement errors and fluctuates greatly across the wafer surface. To solve the problem, a regularization method with continuity constraints was proposed in this study. The mechanisms for the discontinuity of the residual stress distribution and the sensitivity of calculation results to the measurement errors were studied. The influences of the number of subareas of the silicon wafer were investigated and the continuity constraint term was constructed based on the positional relationship of different subareas. Stable and continuous residual stress distribution was successfully obtained after using the proposed regularization method. The method may also be applied to estimate the residual stress from surface deformation for thin substrate plates of other materials. Full article

Nowadays, traffic congestion has become a significant challenge in urban areas and densely populated cities. Real-time traffic signal control is an effective method to reduce traffic jams. This paper proposes a particle swarm optimisation with linearly decreasing weight (LDW-PSO) to tackle the signal intersection control problem, where a finite-interval model and an objective function are built to minimise spoilage time. The performance was evaluated in real-time simulation imitating a crowded intersection in Dalian city (in China) via the SUMO traffic simulator. The simulation results showed that the LDW-PSO outperformed the classical algorithms in this research, where queue length can be reduced by up to 20.4% and average waiting time can be reduced by up to 17.9%. Full article

The conceptualization and framework of smart factories have been intensively studied in previous studies, and the extension to various business areas has been suggested as a future research direction. This paper proposes a method for extending the smart factory concept in the ship building phase to the ship servicing phase through actual examples. In order to expand the study, we identified the differences between manufacturing and maintenance. We proposed a smart transformation procedure, framework, and architecture of a smart maintenance factory. The transformation was a large-scale operation for the entire factory beyond simply applying a single process or specific technology. The transformations were presented through a vessel maintenance depot case and the effects of improvements were discussed. Full article

Research on fault detection (FD) and condition monitoring (CM) of rotating electrical generators for modern wind turbines has addressed a wide variety of technologies. Among these, permanent magnet synchronous generators (PMSGs) and the analysis of their electromagnetic signatures in the presence of faults deserve emphasis in this paper. PMSGs are prominent in the offshore wind industry, and methods for FD and CM of PMSGs based on electromagnetic measurements are extensively discussed in academia. This paper is a concise review of FD and CM in wind turbines and PMSGs. Terminology and fundamentals of PMSG’s operation are introduced first, aiming to offer an easy read and good reference to a broad audience of engineers and data scientists. Experience and research challenges with stator winding failures are also discussed. Full article

With its standardized and unified interface, the quick-change fixture is an important part for maintaining high efficiency without compensation of precision in the metal-turning process because it can conveniently realize high-precision repeated clamping and multi-station conversion without complex positioning and adjustment steps. However, the existing quick-change fixture products and related research cannot fully meet the needs of repeatability and applicability raised from ultra-precision, single-point diamond turning with ultra-high accuracy and ultra-small depth of cut. In this paper, we develop a quick-change fixture for ultra-precision diamond turning, in which the end-toothed disc acts as the positioning element. Specifically, the main parameters of two key components of the end-toothed disc and slotted disc spring are calculated analytically to ensure the positioning accuracy of the designed fixture used in the rotation condition, which is further ensured by controlling the machining tolerance of the tooth profile of the end-toothed disc. Additionally, finite element simulations are performed to investigate the static and modal states of the quick-change fixture, which demonstrate a maximum deformation of about 0.9 μm and a minimum natural frequency of 5655.9 Hz for the designed fixture. Two high-precision sensors are used to detect the radial jump and end run-out values after repeated clamping actions, which are employed to verify the repetitive positioning accuracy of the fixture. Subsequent finite-element simulation of the clamping of small-diameter copper bar, as well as the diamond turning experiment, jointly demonstrate that the designed fixture can achieve a precision of 1 μm. Current work provides an effective quick-change fixture to reduce the deformation of a weak-stiffness workpiece caused by clamping deformation in ultra-precision diamond cutting. Full article

The bearing temperature forecasting provide can provide early detection of the gearbox operating status of wind turbines. To achieve high precision and reliable performance in bearing temperature forecasting, a novel hybrid model is proposed in the paper, which is composed of three phases. Firstly, the variational mode decomposition (VMD) method is employed to decompose raw bearing temperature data into several sub-series with different frequencies. Then, the SAE-GMDH method is utilized as the predictor in the subseries. The stacked autoencoder (SAE) is for the low-latitude features of raw data, while the group method of data handling (GMDH) is applied for the sub-series forecasting. Finally, the imperialist competitive algorithm (ICA) optimizes the weights for subseries and combines them to achieve the final forecasting results. By analytical investigation and comparing the final prediction results in all experiments, it can be summarized that (1) the proposed model has achieved excellent prediction outcome by integrating optimization algorithms with predictors; (2) the experiment results proved that the proposed model outperformed other selective models, with higher accuracies in all datasets, including three state-of-the-art models. Full article

The design and development of space instruments are considered to be distinct from that of other products. It is because the key considerations are vastly different from those that govern the use of products on planet earth. The service life of a space instrument, its use in extreme space environments, size, weight, cost, and the complexity of maintenance must all be considered. As a result, more innovative ideas and resource support are required to assist mankind in space exploration. This article reviews the impact of product design and innovation on the development of space instruments. Using a systematic literature search review and classification, we have identified over 129 papers and finally selected 48 major articles dealing with space instrument product innovation design. According to the studies, it is revealed that product design and functional performance is the main research focuses on the studied articles. The studies also highlighted various factors that affect space instrument manufacturing or fabrication, and that innovativeness is also the key in the design of space instruments. Lastly, the product design is important to affect the reliability of the space instrument. This review study provides important information and key considerations for the development of smart manufacturing technologies for space instruments in the future. Full article

This paper addresses the problem of instance-level 6DoF pose estimation from a single RGBD image in an indoor scene. Many recent works have shown that a two-stage network, which first detects the keypoints and then regresses the keypoints for 6d pose estimation, achieves remarkable performance. However, the previous methods concern little about channel-wise attention and the keypoints are not selected by comprehensive use of RGBD information, which limits the performance of the network. To enhance RGB feature representation ability, a modular Split-Attention block that enables attention across feature-map groups is proposed. In addition, by combining the Oriented FAST and Rotated BRIEF (ORB) keypoints and the Farthest Point Sample (FPS) algorithm, a simple but effective keypoint selection method named ORB-FPS is presented to avoid the keypoints appear on the non-salient regions. The proposed algorithm is tested on the Linemod and the YCB-Video dataset, the experimental results demonstrate that our method outperforms the current approaches, achieves ADD(S) accuracy of 94.5% on the Linemod dataset and 91.4% on the YCB-Video dataset. Full article

Climate changes and the lack of running water across vast territories require the massive use of pumping systems, often powered by solar energy sources. In this context, simple drives with high-efficiency motors can be expected to take hold. It is important to emphasise that simplicity does not necessarily lie in the control algorithm itself, but in the absence of complex manual calibration. These characteristics are met by synchronous reluctance motors provided that the calibration of the current loops is made autonomous. The goal of the present research was the development of a current control algorithm for reluctance synchronous motors that does not require an explicit model of the motor, and that self-calibrates in the first moments of operation without the supervision of a human expert. The results, both simulated and experimental, confirm this ability. The proposed algorithm adapts itself to different motor types, without the need for any initial calibration. The proposed technique is fully within the paradigm of smarter electrical drives, which, similarly to today’s smartphones, offer advanced performance by making any technological complexity transparent to the user. Full article

The reinforcement learning control of an underground loader was investigated in a simulated environment by using a multi-agent deep neural network approach. At the start of each loading cycle, one agent selects the dig position from a depth camera image of a pile of fragmented rock. A second agent is responsible for continuous control of the vehicle, with the goal of filling the bucket at the selected loading point while avoiding collisions, getting stuck, or losing ground traction. This relies on motion and force sensors, as well as on a camera and lidar. Using a soft actor–critic algorithm, the agents learn policies for efficient bucket filling over many subsequent loading cycles, with a clear ability to adapt to the changing environment. The best results—on average, 75% of the max capacity—were obtained when including a penalty for energy usage in the reward. Full article

Three-phase induction motors (IMs) are the main workhorse in industry due to their many advantages as compared to other types of industrial motors. However, the efficiency and lifetime of IMs can be considerably affected by some operating conditions, in particular those related to unbalanced supply voltages (USV), which is quite a common condition in industrial plants. Therefore, early detection and a precise severity estimation of the USV for all working conditions can prevent major breakdowns and increase reliability and safety of industrial facilities. This paper proposes a reliable method allowing for a precise and online detection of the USV condition, by monitoring a pertinent indicator calculated using the voltage symmetrical components. The effectiveness of the proposed method is validated experimentally for several different working conditions, and a comparison with other indicators available in the literature is also performed. Full article

The continuous evolution of modern technology has led to the creation of increasingly complex and advanced systems. This has been also reflected in the technology of Unmanned Aerial Vehicles (UAVs), where the growing demand for more reliable performance necessitates the development of sophisticated techniques that provide fault diagnosis and fault tolerance in a timely and accurate manner. Typically, a UAV consists of three types of subsystems: actuators, main structure and sensors. Therefore, a fault-monitoring system must be specifically designed to supervise and debug each of these subsystems, so that any faults can be addressed before they lead to disastrous consequences. In this survey article, we provide a detailed overview of recent advances and studies regarding fault diagnosis, Fault-Tolerant Control (FTC) and anomaly detection for UAVs. Concerning fault diagnosis, our interest is mainly focused on sensors and actuators, as these subsystems are mostly prone to faults, while their healthy operation usually ensures the smooth and reliable performance of the aerial vehicle. Full article

Today, small- and medium-sized enterprises (SMEs) play an important role in the economy of societies. Although environmental factors, such as COVID-19, as well as non-environmental factors, such as equipment failure, make these industries more vulnerable, they can be minimized by better understanding the concerns and threats these industries face. Only a few SMEs have the capacity to implement the innovative manufacturing technologies of Industry 4.0. The system must be highly adaptable to any equipment, have low costs, avoid the need of doing complex integrations and setups, and have future reliability due to the rapid growth of technology. The goal of this study was to provide an overview of past articles (2010–2020), highlighting the major expectations, requirements, and challenges for SMEs regarding the implementation of predictive maintenance (PdM). The proposed solutions to meet these expectations, requirements, and challenges are discussed. In general, in this study, we attempted to overcome the challenges and limitations of using smart manufacturing—PdM, in particular—in small- and medium-sized enterprises by summarizing the solutions offered in different industries and with various conditions. Moreover, this literature review enables managers and stakeholders of organizations to find solutions from previous studies for a specific category, with consideration for their expectations and needs. This can be significantly helpful for small- and medium-sized organizations to save time due to time-consuming maintenance processes. Full article

Real-time monitoring of the cutting force in the machining process is critical for improving machining accuracy, optimizing the machining process, and optimizing tool lifetime; however, the dynamometers are too expensive to be widely used by machine tool users. Therefore, this paper presents a simple and cheap apparatus—a smart tool holder—to measure the cutting force of turning tools in the finishing turning. The apparatus does not change the structure of the turning tool. It consists of a tool holder and a piezoresistive force sensor foil, and transmits the signal through Bluetooth wireless communication. Instead of dealing with the circuit hardware, this paper uses the Artificial Neural Network (ANN) model to successfully calibrate the warm-up shift problem of the piezoresistive force sensor. Such a software method is simple, and considerably cheaper than the hardware method. For the force measurement capability of the smart tool holder, the cross-interference between orthogonal forces are very small and thus can be ignored. The force reading of the smart tool holder possesses high repeatability for the same turning parameters and high accuracy within the experiment groups. The authors apply the smart tool holder to cut the low carbon steel S15C, and to determine its specific cutting force in fine turning. The resulting fine turning force model agrees very well with the measurement. Its mean absolute deviation is 3.87% and its standard deviation is 1.55%, which reveals that the accuracy and precision of the smart tool holder and the fine turning force model are both good. Full article

Stretchable, skin-interfaced, and wearable strain sensors have risen in recent years due to their wide-ranging potential applications in health-monitoring devices, human motion detection, and soft robots. High aspect ratio (AR) silver nanowires (AgNWs) have shown great potential in the flexible and stretchable strain sensors due to the high conductivity and flexibility of AgNW conductive networks. Hence, this work aims to fabricate highly stretchable, sensitive, and linear kirigami strain sensors with high AR AgNWs. The AgNW synthesis parameters and process windows have been identified by Taguchi’s design of experiment and analysis. Long AgNWs with a high AR of 1556 have been grown at optimized synthesis parameters using the one-pot modified polyol method. Kirigami sensors were fabricated via full encapsulation of AgNWs with Ecoflex silicon rubber. Kirigami-patterned strain sensors with long AgNWs show high stretchability, moderate sensitivity, excellent linearity (R² = 0.99) up to 70% strain and can promptly detect finger movement without obvious hysteresis. Full article

Thermal error is one of the main sources of machining error of machine tools. Being a key component of the machine tool, the spindle will generate a lot of heat in the machining process and thereby result in a thermal error of itself. Real-time measurement of thermal error will interrupt the machining process. Therefore, this paper presents a machine learning model to estimate the thermal error of the spindle from its feature temperature points. The authors adopt random forests and Gaussian process regression to model the thermal error of the spindle and Pearson correlation coefficients to select the feature temperature points. The result shows that random forests collocating with Pearson correlation coefficients is an efficient and accurate method for the thermal error modeling of the spindle. Its accuracy reaches to 90.49% based on only four feature temperature points—two points at the bearings and two points at the inner housing—and the spindle speed. If the accuracy requirement is not very onerous, one can select just the temperature points of the bearings, because the installation of temperature sensors at these positions is acceptable for the spindle or machine tool manufacture, while the other positions may interfere with the cooling pipeline of the spindle. Full article

Attitude estimation is a basic task for most spacecraft missions in aerospace engineering and many Kalman type attitude estimators have been applied to the guidance and navigation of spacecraft systems. By building the attitude dynamics on matrix Lie groups, the invariant Kalman filter (IKF) was developed according to the invariance properties of symmetry groups. However, the Gaussian noise assumption of Kalman theory may be violated when a spacecraft maneuvers severely and the process noise might be heavy-tailed, which is prone to degrade IKF’s performance for attitude estimation. To address the attitude estimation problem with heavy-tailed process noise, this paper proposes a hierarchical Gaussian state-space model for invariant Kalman filtering: The probability density function of state prediction is defined based on student’s t distribution, while the conjugate prior distributions of the scale matrix and degrees of freedom (dofs) parameter are respectively formulated as the inverse Wishart and Gamma distribution. For the constructed hierarchical Gaussian attitude estimation state-space model, the Lie groups rotation matrix of spacecraft attitude is inferred together with the scale matrix and dof parameter using the variational Bayesian iteration. Numerical simulation results illustrate that the proposed approach can significantly improve the filtering robustness of invariant Kalman filter for Lie groups spacecraft attitude estimation problems with heavy-tailed process uncertainty. Full article

Spiral bevel gears are known for their smooth operation and high load carrying capability; therefore, they are an important part of many transmission systems that are designed for high speed and high load applications. Due to high contact ratio and complex vibration signal, their fault detection is really challenging even in the case of serious defects. Therefore, spiral bevel gears have rarely been used as benchmarking for gears’ fault diagnosis. In this research study, Artificial Intelligence (AI) techniques have been used for fault detection and fault severity level identification of spiral bevel gears under different operating conditions. Although AI techniques have gained much success in this field, it is mostly assumed that the operating conditions under which the trained AI model is deployed for fault diagnosis are same compared to those under which the AI model was trained. If they differ, the performance of AI model may degrade significantly. In order to overcome this limitation, in this research study, an effort has been made to find few robust features that show minimal change due to changing operating conditions; however, they are fault discriminating. Artificial neural network (ANN) and K-nearest neighbors (KNN) are used as classifiers and both models are trained and tested by using the selected robust features for fault detection and severity assessment of spiral bevel gears under different operating conditions. A performance comparison between both classifiers is also carried out. Full article

With the rapid development of modern manufacturing processes, ultra-precision structured freeform surfaces are being widely explored for components with special surface functioning. Measurement of the 3D surface form of structured specular objects remains a challenge because of the complexity of the surface form. Benefiting from a high dynamic range and large measuring area, phase measurement deflectometry (PMD) exhibits great potential in the inspection of the specular surfaces. However, the PMD is insensitive to object height, which leads to the PMD only being used for smooth specular surface measurement. Direct phase measurement deflectometry (DPMD) has been introduced to measure structured specular surfaces, but the surface form measurement resolution and accuracy are limited. This paper presents a method named stereo-DPMD for measuring structured specular objects by introducing a stereo deflectometor into DPMD, so that it combines the advantages of slope integration of the stereo deflectometry and discontinuous height measurement from DPMD. The measured object is separated into individual continuous regions, so the surface form of each region can be recovered precisely by slope integration. Then, the relative positions between different regions are evaluated by DPMD system to reconstruct the final 3D shape of the object. Experimental results show that the structured specular surfaces can be measured accurately by the proposed stereo-DPMD method. Full article

In this paper, we propose an adaptive data-driven control approach for linear time varying systems, affected by bounded measurement noise. The plant to be controlled is assumed to be unknown, and no information in regard to its time varying behaviour is exploited. First, using set-membership identification techniques, we formulate the controller design problem through a model-matching scheme, i.e., designing a controller such that the closed-loop behaviour matches that of a given reference model. The problem is then reformulated as to derive a controller that corresponds to the minimum variation bounding its parameters. Finally, a convex relaxation approach is proposed to solve the formulated controller design problem by means of linear programming. The effectiveness of the proposed scheme is demonstrated by means of two simulation examples. Full article

The precision spool valve is the core component of the electro-hydraulic servo control system, and its performance has an important influence on the flight control of aviation and aerospace products. The non-uniform surface topography error causes a non-uniform mating gap field inside the spool valve, which causes oil leakage and leads to deterioration of the spool valve performance. However, the current oil leakage calculation method only considers the influence of size errors, which is not comprehensive. Thus, how to characterize the mating behavior of the spool valve and its effect on oil leakage with consideration of surface topography errors is the key to evaluating the performance of the spool valve. This paper proposes a new way of analyzing the mating performance of precision spool valves, which considers the surface topography errors based on digital twin technology. Firstly, a general framework for the analysis of mating performance of precision spool valve based on a digital twin is proposed. Then, key technologies of assembly interface geometry modeling, matching behavior modeling and performance analysis are studied. Finally, a quantitative correlation between the mating parameters and the oil leakage of the precision spool valve is revealed. The method is tested on a practical case. This proposed method can provide theoretical support for the accurate prediction and evaluation of the mating performance of the precision spool valve. Full article

Mathematical models have been widely used in the study of rotating machines. Their application in dynamics has eased further research since they can avoid time-consuming and exorbitant experimental processes to simulate different faults. The earlier vibration-based machine-learning (VML) model for fault diagnosis in rotating machines was developed by optimising the vibration-based parameters from experimental data on a rig. Therefore, a mathematical model based on the finite-element (FE) method is created for the experimental rig, to simulate several rotor-related faults. The generated vibration responses in the FE model are then used to validate the earlier developed fault diagnosis model and the optimised parameters. The obtained results suggest the correctness of the selected parameters to characterise the dynamics of the machine to identify faults. These promising results provide the possibility of implementing the VML model in real industrial systems. Full article

Powered Assistive Devices (PADs) have been proposed to enable repetitive, user-oriented gait rehabilitation. They may include torque controllers that typically require reference joint torque trajectories to determine the most suitable level of assistance. However, a robust approach able to automatically estimate user-oriented reference joint torque trajectories, namely ankle torque, while considering the effects of varying walking speed, body mass, and height on the gait dynamics, is needed. This study evaluates the accuracy and generalization ability of two Deep Learning (DL) regressors (Long-Short Term Memory and Convolutional Neural Network (CNN)) to generate user-oriented reference ankle torque trajectories by innovatively customizing them according to the walking speed (ranging from 1.0 to 4.0 km/h) and users’ body height and mass (ranging from 1.51 to 1.83 m and 52.0 to 83.7 kg, respectively). Furthermore, this study hypothesizes that DL regressors can estimate joint torque without resourcing electromyography signals. CNN was the most robust algorithm (Normalized Root Mean Square Error: 0.70 ± 0.06; Spearman Correlation: 0.89 ± 0.03; Coefficient of Determination: 0.91 ± 0.03). No statistically significant differences were found in CNN accuracy (p-value > 0.05) whether electromyography signals are included as inputs or not, enabling a less obtrusive and accurate setup for torque estimation. Full article

This paper presents the analytical calculation of the heat transfer coefficient of a complex housing shape of a Totally Enclosed Fan-Cooled (TEFC) industrial machine when it works below 20% of its nominal speed or close to stall. Therefore, passive cooling is dominant, and most of the heat is extracted by the combination of natural convection and radiation phenomena. Under these conditions, the area-based composite approach was used for the development of the analytical calculation method. A test rig using a TEFC Synchronous Reluctance Motor (SynRM) was constructed, and the collected experimental data was used to validate the proposed analytical method successfully. Full article

This paper presents a framework for the motion planning and control of redundant manipulators with the added task of collision avoidance. The algorithms that were previously studied and tested by the authors for planar cases are here extended to full mobility redundant manipulators operating in a three-dimensional workspace. The control strategy consists of a combination of off-line path planning algorithms with on-line motion control. The path planning algorithm is used to generate trajectories able to avoid fixed obstacles detected before the robot starts to move; this is based on the potential fields method combined with a smoothing interpolation that exploits Bézier curves. The on-line motion control is designed to compensate for the motion of the obstacles and to avoid collisions along the kinematic chain of the manipulator; this is realized using a velocity control law based on the null space method for redundancy control. Furthermore, an additional term of the control law is introduced which takes into account the speed of the obstacles, as well as their position. In order to test the algorithms, a set of simulations are presented: the redundant collaborative robot KUKA LBR iiwa is controlled in different cases, where fixed or dynamic obstacles interfere with its motion. The simulated data show that the proposed method for the smoothing of the trajectory can give a reduction of the angular accelerations of the motors of the order of $90\%$, with an increase of less than $15\%$ of the calculation time. Furthermore, the dependence of the on-line control law on the speed of the obstacle can lead to reductions in the maximum speed and acceleration of the joints of approximately $50\%$ and $80\%$, respectively, without significantly increasing the computational effort that is compatible for transferability to a real system. Full article

As an essential perceptual device, the tactile sensor can efficiently improve robot intelligence by providing contact force perception to develop algorithms based on contact force feedback. However, current tactile grasping technology lacks high-performance sensors and high-precision grasping prediction models, which limits its broad application. Herein, an intelligent robot grasping system that combines a highly sensitive tactile sensor array was constructed. A dataset that can reflect the grasping contact force of various objects was set up by multiple grasping operation feedback from a tactile sensor array. The stability state of each grasping operation was also recorded. On this basis, grasp stability prediction models with good performance in grasp state judgment were proposed. By feeding training data into different machine learning algorithms and comparing the judgment results, the best grasp prediction model for different scenes can be obtained. The model was validated to be efficient, and the judgment accuracy was over 98% in grasp stability prediction with limited training data. Further, experiments prove that the real-time contact force input based on the feedback of the tactile sensor array can periodically control robots to realize stable grasping according to the real-time grasping state of the prediction model. Full article

The study provides a solution to a dimensional synthesis problem for a hexapod-type reconfigurable parallel mechanism, which can change its configuration to realize different trajectories of its output link while having a single drive. The work presents an original procedure to find the dimensions of some mechanism’s links and their initial configuration to reproduce these trajectories. After describing the mechanism, the paper examines kinematic relations representing the basis for the subsequent synthesis algorithm. Next, the obtained expressions are extended and provide a system of equations to be solved. The structure of this equation system allows it to be solved effectively by numerical methods, which is demonstrated with an example. The proposed algorithm of dimensional synthesis does not require solving the optimization problems, in contrast to the familiar methods of dimensional synthesis of parallel mechanisms. Further, the suggested approach to the synthesis problem allows finding solution in a fast and computationally efficient manner. Full article

Aiming at solving the problem of dual resource constrained flexible job shop scheduling problem (DRCFJSP) with differences in operating time between operators, an artificial intelligence (AI)-based DRCFJSP optimization model is developed in this paper. This model introduces the differences between the loading and unloading operation time of workers before and after the process. Subsequently, the quantum genetic algorithm (QGA) is used as the carrier; the process is coded through quantum coding; and the niche technology is used to initialize the population, adaptive rotation angle, and quantum mutation strategy to improve the efficiency of the QGA and avoid premature convergence. Lastly, through the Kacem standard calculation example and the reliability analysis of the factory workshop processing process example, performance evaluation is conducted to show that the improved QGA has good convergence and does not fall into premature ability, the improved QGA can solve the problem of reasonable deployment of machines and personnel in the workshop, and the proposed method is more effective for the DRCFJSP than some existing methods. The findings can provide a good theoretical basis for actual production and application. Full article

Wind turbines are rotating machines which are subjected to non-stationary conditions and their power depends non-trivially on ambient conditions and working parameters. Therefore, monitoring the performance of wind turbines is a complicated task because it is critical to construct normal behavior models for the theoretical power which should be extracted. The power curve is the relation between the wind speed and the power and it is widely used to monitor wind turbine performance. Nowadays, it is commonly accepted that a reliable model for the power curve should be customized on the wind turbine and on the site of interest: this has boosted the use of SCADA for data-driven approaches to wind turbine power curve and has therefore stimulated the use of artificial intelligence and applied statistics methods. In this regard, a promising line of research regards multivariate approaches to the wind turbine power curve: these are based on incorporating additional environmental information or working parameters as input variables for the data-driven model, whose output is the produced power. The rationale for a multivariate approach to wind turbine power curve is the potential decrease of the error metrics of the regression: this allows monitoring the performance of the target wind turbine more precisely. On these grounds, in this manuscript, the state-of-the-art is discussed as regards multivariate SCADA data analysis methods for wind turbine power curve modeling and some promising research perspectives are indicated. Full article

Power skiving is a new gear cutting process that has been recognized to provide a step change in the production rate achieved in the machining of high-precision internal and external involute gears. The process is based on a continuous generating meshing between the workgear and the cutting tool. Understanding of the loads applied in the cutting tool, and therefore some of the sources of tool wear, have not been thoroughly understood. This paper presents a novel model that is able to predict with high accuracy the cutting forces in the power skiving process. The model is based on a solid modelling simulation algorithm that produces high-fidelity solid bodies that are used for the calculations. The results of the model have been experimentally validated. A series of process maps are also produced to assist in the identification of the optimal machining parameters. Full article

This paper addresses the design of a novel mechatronic device for saffron harvesting. The main proposed challenge consists of proposing a new paradigm for semi-automatic harvesting of saffron flowers. The proposed novel solution is designed for being easily portable with user-friendly and cost-oriented features and with a fully electric battery-powered actuation. A preliminary concept design has been proposed as based on a specific novel cam mechanism in combination with an elastic spring for fulfilling the detachment of the flowers from their stems. Numerical calculations and simulations have been carried out to complete the full design of a proof-of-concept prototype. Preliminary experimental tests have been carried out to demonstrate the engineering feasibility and effectiveness of the proposed design solutions, whose concept has been submitted for patenting. Full article

In this article, technology for producing wire and rod solder from 52In-48Sn alloy has been developed and investigated in the conditions of small-scale production. The use of direct extrusion of wire and rods instead of traditional technology for producing solder, which includes pressing, rolling and drawing, can significantly reduce the fleet of required equipment. Using only a melting furnace and a hydraulic press, solder wires and rods can be produced in various sizes. Shortening the production cycle allows you to quickly fulfill small orders and be competitive in sales. This article develops a mathematical model of direct extrusion, which allows you to calculate the extrusion ratio, extrusion speed and pressing force. The results of modeling the process of extrusion of wire Ø2.00 mm and rods Ø8.0 mm made of 52In-48Sn alloy are presented. The temperature of the solder and the tool is simulated in software QForm based on the finite element method. Experimental results of manufacturing Ø2.0 mm solder wire and Ø8.0 mm rods are presented. The microstructure of the direct extruded solder is a eutectic of phases γ and β. Energy-dispersive X-ray spectroscopy (EDS) mapping of the 52In-48Sn alloy showed that the solder obtained by direct extrusion has a uniform distribution of structural phases. The developed technology can be used in the manufacture of wires and rods from other low-melting alloys. Full article

This paper discusses the author-developed novel method for the detection of buried metal objects that combines two basic subsurface sensing methods: one based on changes in the electromagnetic field parameters as induced by the inner or surficial impedance of the medium when affected by a propagating magnetic field; and one based on changes in the input impedance of the receiver as induced by the electromagnetic properties of the probed medium. The proposed method utilizes three instrumentation channels: two primary channels come from the ferrite magnetic antenna (the receiver), where the first channel is used to measure the current voltage amplitude of the active input signal component, while the second channel measures the current voltage amplitude of the reactive input signal component; an additional (secondary) channel comes from the emitting frame antenna (the transmitter) to measure the current amplitude of the exciting current. This data redundancy proves to significantly improve the reliability and accuracy of detecting buried metal objects. Implementation of the computational procedures for the proposed method helped to detect and identify buried objects by their specific electrical conductance and magnetic permeability, while also locating them depth-wise. The research team has designed an induction probe that contains two mutually orthogonal antennas (a frame transmitter and ferrite receiver); the authors herein propose a metal detector design that implements the proposed induction sensing method. Experimental research proved the developed combined method for searching for buried metal objects efficient and well-performing. Full article

The present paper deals with the optimization of the three components of cutting forces and the Material Removal Rate (MRR) in the turning of AISI 5140 steel. The Harmonic Artificial Bee Colony Algorithm (H-ABC), which is an improved nature-inspired method, was compared with the Harmonic Bee Algorithm (HBA) and popular methods such as Taguchi’s S/N ratio and the Response Surface Methodology (RSM) in order to achieve the optimum parameters in machining applications. The experiments were performed under dry cutting conditions using three cutting speeds, three feed rates, and two depths of cuts. Quadratic regression equations were identified as the objective function for HBA to represent the relationship between the cutting parameters and responses, i.e., the cutting forces and MRR. According to the results, the RSM (72.1%) and H-ABC (64%) algorithms provide better composite desirability compared to the other techniques, namely Taguchi (43.4%) and HBA (47.2%). While the optimum parameters found by the H-ABC algorithm are better when considering cutting forces, RSM has a higher success rate for MRR. It is worth remarking that H-ABC provides an effective solution in comparison with the frequently used methods, which is promising for the optimization of the parameters in the turning of new-generation materials in the industry. There is a contradictory situation in maximizing the MRR and minimizing the cutting power simultaneously, because the affecting parameters have a reverse effect on these two response parameters. Comparing different types of methods provides a perspective in the selection of the optimum parameter design for industrial applications of the turning processes. This study stands as the first paper representing the comparative optimization approach for cutting forces and MRR. Full article

This review is dedicated to balancing methods that are used to solve the rotor-balancing problem. To ensure a stable operation over an operating speed range, it is necessary to balance a rotor. The traditional methods, including the influence coefficient method (ICM) and the modal balancing method (MBM) are introduced, and the research progress, operation steps, advantages and disadvantages of these methods are elaborated. The classification of new balancing methods is reviewed. Readers are expected to obtain an overview of the research progress of existing balancing methods and the directions for future studies. Full article