Machines, Volume 11, Issue 8 (August 2023) – 77 articles

In order to perform multi-degree-of-freedom motion measurements of marine machinery, such as ship-borne mechanical platforms, in an absolute environment without a reference, absolute measurement methods using acceleration sensors and tilt gyroscopes are typically employed. However, the influence of wave forces on ship-borne mechanical platforms can cause coupling between different degrees of freedom, resulting in significant measurement disturbances that make efficient computation and real-time analysis challenging. To address these challenges, a correction method for the motion measurement of the ship-borne mechanical platform based on multi-sensor fusion is proposed by analyzing the influence of the inclination angle of the ship-borne mechanical platform on the sensor measurement based on the working principles of the acceleration sensor and angle sensor. In this article, we first analyzed the influence of the inclination angle on the integral effect in the heave direction. Then, we proposed a configuration using four groups of acceleration sensors to correct the integral effect. Finally, the optimal inclination angle is determined through Kalman filtering based on the measured values of the angle sensors and estimated values from the acceleration sensor sets. Experiments have proved that the average error of the corrected heave displacement signal is 25.34 mm, which is better than the integral displacement signal of a single acceleration sensor. At the same time, we use the acceleration sensor to calculate the roll angle and pitch angle of the ship-borne mechanical platform and combine it with the angle sensor signal to perform Kalman filtering. This filters out the errors caused by the shaking and instability of the mechanical platform and can more accurately estimate the true inclination of the platform. Therefore, this method can enhance the precision and accuracy of ship-borne mechanical platform motion signal acquisition, providing more valuable experimental data for research in marine engineering and related fields. Full article

The health status of equipment is of paramount importance during the operation of nuclear power plants. The occurrence of faults not only leads to significant economic losses but also poses risks of casualties and even major accidents, with unimaginable consequences. This paper proposed a deep learning framework called PT-Informer for fault prediction, detection, and localization in order to address the challenges of online monitoring of the operating health of nuclear steam turbines. Unlike traditional approaches that involve separate design and execution of feature extraction for fault diagnosis, classification, and prediction, PT-Informer aims to extract fault features from the raw vibration signal and perform ultra-real-time fault prediction prior to their occurrence. Specifically, the encoding and decoding structure in PT-Informer ensures the capture of temporal dependencies between input features, enabling accurate time series prediction. Subsequently, the predicted data are utilized for fault detection using PCA in the PT-Informer framework, aiming to assess the likelihood of equipment failure in the near future. In the event of potential future failures, t-SNE is utilized to project high-dimensional data into a lower-dimensional space, facilitating the identification of clusters or groups associated with different fault types or operational conditions, thereby achieving precise fault localization. Experimental results on a nuclear steam turbine rotor demonstrate that PT-Informer outperformed the traditional GRU with a 4.94% improvement in R2 performance for prediction. Furthermore, compared to the conventional model, the proposed PT-Informer enhanced the fault classification accuracy of the nuclear steam turbine rotor from 97.4% to 99.6%. Various comparative experiments provide strong evidence for the effectiveness of PT-Informer framework in the diagnosis and prediction of nuclear steam turbine. Full article

This paper addresses the lateral motion control of a supercavitating vehicle and studies its ability to maneuver. According to the unique hydrodynamic characteristics of the supercavitating vehicle, highly coupled nonlinear 6-degree-of-freedom (DOF) dynamic and kinematic models are constructed considering time-delay effects. A control scheme utilizing radial basis function (RBF) neural-network-(NN)-based adaptive sliding with planing force avoidance is proposed to simultaneously control the longitudinal stability and lateral motion of the supercavitating vehicle in the presence of external ocean-induced disturbances. The online estimation of nonlinear disturbances is conducted in real time by the designed NN and compensated for the dynamic control laws. The adaptive laws of the NN weights and control parameters are introduced to improve the performance of the NN. The least squares method is utilized to solve the actuator control efforts with rolling restriction in real-time online. Rigorous theoretical proofs based on the Lyapunov theory prove the globally asymptotic stability of the proposed controller. Finally, numerical simulations were performed to obtain maximum maneuverability and verify the effectiveness and robustness of the proposed control scheme. Full article

Polyvinyl Alcohol (PVA) is frequently applied as a support material in 3D printing, especially in the crafting of intricate designs and projecting elements. It functions as a water-soluble filament, often paired with materials like ABS or PLA. PVA serves as a momentary scaffold, supporting the jutting segments of a 3D model throughout the printing process. Subsequent to printing, the primary component can be effortlessly isolated by dissolving the PVA support using water. PVA, being a pliable and eco-friendly polymer, is susceptible to moisture. Its aqueous solubility renders it a prime selection for bolstering 3D print structures. In this investigation, equivalent-sized samples were 3D printed utilizing an Ultimaker 3D printer to assess the potency of PVA-generated specimens. Tensile examinations were executed on each sample employing a testing apparatus. The durability of the specimens was notably impacted by the input parameters, specifically the stratum width and stratum thickness. Strength dwindled as stratum width increased, whereas it rose with augmented stratum thickness. A few specimens with heightened stratum width and compromised quality displayed subpar performance during the tensile assessment. The findings unveiled a peak tensile strength of 17.515 MPa and a maximum load of 1600 N. Attaining an optimal degree of material utilization led to a decrease in filament consumption by 8.87 g, all the while upholding a MTS (maximum tensile strength) of 10.078 MPa. Full article

Location identification is a fundamental aspect of advanced mobile robot navigation systems, as it enables establishing meaningful connections between objects, spaces, and actions. Understanding human actions and accurately recognizing their corresponding poses play pivotal roles in this context. In this paper, we present an observation-based approach that seamlessly integrates object detection algorithms, human pose detection, and machine learning techniques to effectively learn and recognize human actions in household settings. Our method entails training machine learning models to identify the common actions, utilizing a dataset derived from the interaction between human pose and object detection. To validate our approach, we assess its effectiveness using a diverse dataset encompassing typical household actions. The results demonstrate a significant improvement over existing techniques, with our method achieving an accuracy of over 95% in classifying eight different actions within household environments.. Furthermore, we ascertain the robustness of our approach through rigorous testing in real-world environments, demonstrating its ability to perform well despite the various challenges of data collection in such settings. The implications of our method for robotic applications are significant, as a comprehensive understanding of human actions is essential for tasks such as semantic navigation. Moreover, our findings unveil promising opportunities for future research, as our approach can be extended to learn and recognize a wide range of other human actions. This perspective, which highlights the potential leverage of these techniques, provides an encouraging path for future investigations in this field. Full article

Current trends in the transportation industry prioritize competitive rivalry, compelling manufacturers to prioritize concepts such as quality and reliability. These concepts are closely associated with public expectations of safety, vehicle lifespan, and trouble-free operation. However, the public must recognize that a vehicle weighing several hundred kilograms, moving at a non-zero speed, only contacts the road surface through a few points (depending on the number of wheels), each no larger than a human palm. Therefore, it is imperative to operate the vehicle in a manner that optimizes the behavior of these contact points. There are situations where drivers find themselves requiring dynamic vehicle handling, often unpredictable with a high degree of uncertainty. Rapid changes in direction become necessary in these cases. Such maneuvers can pose a significant risk of rollover for three-wheeled vehicles. Hence, the vehicle itself should contribute to increased ride safety. This paper presents key findings from the development of an unconventional three-wheeled vehicle utilizing the delta arrangement. Rollover safety for three-wheeled vehicles is currently well-managed, thanks to the utilization of electronic or mechatronic systems in delta-type vehicles to enhance stability. However, these systems require additional components. In contrast, the proposed control system operates solely on a mechanical principle, eliminating operational costs, energy consumption, maintenance expenses, and similar factors. The study also explores the absence of equivalent suspension and steering systems for front-wheel steering. Such designs are lacking in both practical applications and theoretical realms. Analytical and simulation calculations are compared in this study, highlighting the effectiveness of the newly proposed control system in enhancing stability and safety compared to conventional front-wheel suspension systems. Simulation programs provide more realistic results than analytical calculations due to their ability to account for dynamic effects on vehicle components and passengers, which is practically unfeasible in analytical approaches. Furthermore, this study focuses on investigating the fatigue life of material frames subjected to dynamic loading, which is a crucial aspect of ensuring safety. It is essential to have various testing devices to examine the fatigue life of materials under both uniaxial and multiaxial loading conditions. However, obtaining experimental results for fatigue life measurements of specific materials, which can be directly applied to one’s research, poses significant challenges. Hence, the proposed testing device plays a vital role in measuring material fatigue life and advancing the development of unconventional transportation methods. The information about the original testing device aligns perfectly with the article’s emphasis on dynamic analysis. The ultimate objective of all these efforts is to put the vehicle into practical operation for commercial utilization. Full article

The MK circles represent a kinematic synthesis tool for the dimensional synthesis of planar dyads. The tool is uniquely useful in its ability to both find specific dyad solutions and help the designer visualize numerous potential dyad pivot locations in the solution space. Here, the existing understanding of MK circles is summarized for three and four specified motion positions and extended for additional positions. Then the technique is expanded to show its application to MKT circles for triad synthesis, including solution space visualization, ground pivot specification, and multi-loop synthesis of complex mechanisms. These methods are illustrated by a unifying example that provides a sample procedure for applying the MK/MKT circles, and implements each of the aforementioned techniques. The interchangeability of loop-based synthesis approaches is demonstrated by comparing the new methodology to the compatibility linkages technique. Full article

In this paper, a two-rotational degrees of freedom parallel mechanism with five kinematic subchains (3UPS-UPU-S) (U, P, and S stand for universal joints, prismatic joints, and spherical joints) for an aerospace product is introduced, and its kinematic and dynamic characteristics are subsequently analyzed. The kinematic and dynamic analyses of this mechanism are carried out in screw coordinates. Firstly, the inverse kinematics is performed through the kinematic equations established by the velocity screws of each joint to obtain the position, posture, and velocity of each joint within the mechanism. Then, a dynamic modeling method with screw theory for multi-body systems is proposed. In this method, the momentum screws are established by the momentum and moment of momentum according to the fundamentals of screws. By using the kinematic parameters of joints, the dynamic analysis can be carried out through the dynamic equations formed by momentum screws and force screws. This method unifies the kinematic and dynamic analyses by expressing all parameters in screw form. The approach can be employed in the development of computational dynamics because of its simplified and straightforward analysis procedure and its high adaptability for different kinds of multi-body systems. Full article

The presence of excessive background noise in hydrodynamic noise experiments conducted in circulating water tunnels can significantly impact the accuracy and reliability of experimental test results. To address this issue, it is crucial to evaluate and optimize the background noise during the design stage. In this research, acoustic field model and fluid–solid coupling numerical calculation model of circulating water tunnels are established. Utilizing the finite element method, we analyze the flow noise and flow-excited noise resulting from wall pressure pulses in the circulating water tunnel. Furthermore, we conduct a noise contribution analysis and explore strategies for structural vibration noise control. The results demonstrate that both flow noise and flow-excited noise decrease with increasing frequency, with flow-excited noise being the primary component of the tunnel’s background noise. The presence of resonant peaks significantly contributes to the elevated flow-excited noise levels. Moreover, enhancing structural stiffness and damping proves less effective in suppressing low-frequency peaks. Additionally, employing sound measurement pods suspended from the side of the test section for noise measurement exhibits a high error rate at low frequencies. This research provides insights into optimizing background noise in water tunnels, thereby informing future enhancements in tunnel design. Full article

Human–machine shared control of intelligent vehicles is considered an important technology during the industrial application of autonomous driving systems. Among the engineering practices in driver assistance systems, shared steering control is one of the important applications for the human–machine interaction. However, how to deal with human–machine conflicts during emergency scenarios is the main challenge for the controller’s design. Most shared control approaches usually generate machine-oriented results without enough attention to the driver’s reaction. By taking the human driver and machine system as two intelligent agents, this paper proposes a game-based control scheme to achieve a dynamic authority allocation during the lane changing maneuver. Based on the modeling of predicted trajectories of the human driver, a human-intention-based shared steering control is designed to achieve dynamic Nash game equilibrium. Moreover, a human-oriented shared steering mechanism is employed to not only benefit from automated machine assistance, but also make full play of human contributions. Using quantitative comparative analysis in lane changing scenarios with different human–machine conflicts, a better performance by considering both driving comfort and safety is achieved. Full article

Here, a novel hybrid method of intelligent fault identification within complex mechanical systems was proposed using parallel-factor (PARAFAC) theory and adaptive particle swarm optimization (APSO) for a support vector machine (SVM). The parallel-factor multi-scale analysis theory was studied to reconstruct tensor feature information based on a three-dimensional matrix for time, frequency, and spatial vectors. A multi-scale wavelet analysis was used to transform the original multi-channel experimental data acquired from a gearbox into a three-dimensional feature matrix of the multi-level structure. The optimal correspondence among the two-dimensional feature signals in the frequency and time domains for the different fault modes was established by the PARAFAC theory. An intelligent APSO algorithm was developed to obtain the optimal parameter structures of an SVM classifier. A comparison with the existing time–frequency analysis method showed that the proposed hybrid PARAFAC-PSO-SVM diagnosis model effectively eliminated the redundant information in the multi-dimensional tensor features but retained the important components. The PARAFAC-APSO-SVM hybrid diagnostic model achieved fast, accurate, and simple fault-classification and identification results, and could provide theoretical support for the application of the PARAFAC theory to complex mechanical fault diagnosis. Full article

The methodology for assigning and assessing the surface quality is used at various stages of the product life cycle: during the design and technological preparation of production, the production itself, and during the control (testing) of products. The development of advanced technologies requires in situ part control. A non-contact in situ surface roughness measuring system is proposed in this paper. The proposed system utilizes a chromatic confocal sensor, and profile data, waviness data, roughness data, Ra, and Rz parameters are generated in the developed data-processing software. The assembled measuring system based on the chromatic confocal laser sensor showed its performance in assessing the roughness parameter Ra, from 0.34 µm to more than 12 µm, which covers a common range of milling, turning, and grinding. In this range, measurement relative errors can be controlled within 10%. Frequency analysis and correlation analysis of profilograms were performed. Frequency analysis made it possible to establish the dominant frequency components that occur in the profilogram of the samples, while correlation analysis was used to develop a methodology for identifying the deterministic and random components of the processed surface profile signal. The results of the analysis can be further used to develop diagnostic functions for process monitoring based on profilogram estimates, such as the autocorrelation function and the power spectrum density. Full article

Monitoring the machining process is crucial for providing cost-effective, high-quality production and preventing unwanted accidents. This study aims to predict critical machining conditions related to surface roughness and tool breakage in titanium alloy slot milling. The Siemens SINUMERIK EDGE (SE) Box system collects signals from the spindle and axes of a CNC machine tool. In this study, features were extracted from signals in time, frequency, and time–frequency domains. The t-test and the binary whale optimization algorithm (BWOA) were applied to choose the best features and train the support vector machine (SVM) model with validation and training data. The SVM hyperparameters were optimized simultaneously with feature selection, and the model was tested with test data. The proposed model accurately predicted critical machining conditions for unbalanced datasets. The classification model indicates an average recall, precision, and accuracy of 80%, 86%, and 95%, respectively, when predicting workpiece quality and tool breakage. Full article

Computer vision technology is increasingly being widely applied in automated industrial production. However, the accuracy of workpiece detection is the bottleneck in the field of computer vision detection technology. Herein, a new object detection and classification deep learning algorithm called CSW-Yolov7 is proposed based on the improvement of the Yolov7 deep learning network. Firstly, the CotNet Transformer structure was combined to guide the learning of dynamic attention matrices and enhance visual representation capabilities. Afterwards, the parameter-free attention mechanism SimAM was introduced, effectively enhancing the detection accuracy without increasing computational complexity. Finally, using WIoUv3 as the loss function effectively mitigated many negative influences during training, thereby improving the model’s accuracy faster. The experimental results manifested that the [email protected] of CSW-Yolov7 reached 93.3%, outperforming other models. Further, this study also designed a polyhedral metal workpiece detection system. A large number of experiments were conducted in this system to verify the effectiveness and robustness of the proposed algorithm. Full article

This paper aims to investigate the possibility of combining humanoid robots, particularly the NAO robot, with a mobile application to enhance the educational experiences of children with autism spectrum disorder (ASD). The NAO robot, interfaced with a mobile app, serves as a socially assistive robotic (SAR) tool in the classroom. The study involved two groups of children aged three to six years old, exhibiting mild to moderate ASD symptoms. While the experimental group interacted with the NAO robot, the control group followed the standard curriculum. Initial findings showed that students in the experimental group exhibited higher levels of engagement and eye contact. However, certain limitations were identified, including the NAO robot’s limited capacity for concurrent interactions, language difficulties, battery life, and internet access. Despite these limitations, the study highlights the potential of robots and AI in addressing the particular educational requirements of children with ASD. Future research should focus on overcoming these obstacles to maximize the advantages of this technology in ASD education. Full article

The study focused on the development of -gas turbine full- and part-load operation diagnostics. The gas turbine performance model was developed using commercial software and validated using the engine manufacturer data. Upon the validation, fouling, erosion, and variable inlet guide vane drift were simulated to generate faulty data for the diagnostics development. Because the data from the model was noise-free, sensor noise was added to each of the diagnostic set parameters to reflect the actual scenario of the field operation. The data was normalized. In total, 13 single, and 61 double, classes, including 1 clean class, were prepared and used as input. The number of observations for single faults diagnostics were 1092, which was 84 for each class, and 20,496 for double faults diagnostics, which was 336 for each class. Twenty-eight machine learning techniques were investigated to select the one which outperformed the others, and further investigations were conducted with it. The diagnostics results show that the neural network group exhibited better diagnostic accuracy at both full- and part-load operations. The test results and its comparison with literature results demonstrated that the proposed method has a satisfactory and reliable accuracy in diagnosing the considered fault scenarios. The results are discussed, following the plots. Full article

This paper presents a synchronous proportional derivative (PD) control method using a time delay estimator (SPD-TDE) for a four-degree-of-freedom (DOF) parallel robot in practice. The proposed control is a method that is developed from a synchronous PD control method combined with a time delay estimator to guarantee the tracking objectives and synchronous requirements of the robot. Firstly, the synchronous PD control method is designed by defining cross-coupling errors. A cross-coupling error is determined by incorporating the tracking error and deviation of tracking error among two adjacent joints or synchronous errors. Then, the asynchronous problem between the kinematic chains is solved and guarantees that the goal of synchronicity is achieved. Consequently, to improve the tracking performance of the robot, a time delay estimator is used to estimate and eliminate the uncertainty components of the system, such as modeling errors and actuator faults. In addition, the Lyapunov theory is also used to demonstrate the stability and robustness of the proposed control method. Finally, a testbench 4-DOF parallel robot is built, and the controllers are embedded in the control board from MATLAB Simulink using the Waijung block set library to operate the robot preset trajectory tracking. The experimental results of the proposed control method for the 4-DOF parallel robot are compared with those obtained using other controllers to prove its effectiveness. Full article

With the rapid development of science and technology, high-resolution remote sensing cameras are now widely used in various fields. At the beginning of camera attitude adjustment, residual torque due to state changes can affect platform stability and lead to the degradation of imaging quality. This paper analyzes the effect of external disturbances on the attitude of the satellite platform according to the Newton–Euler method. In order to effectively realize the self-balancing of the torque of the remote sensing camera rotary table and eliminate the influence of the residual torque on the stability of the satellite platform, this study designs a torque balancing mechanism for the two-axis rotary table of a remote sensing camera based on the first-generation balancing mechanism. Firstly, this paper provides a detailed analysis of the mechanism equilibrium principle from the theoretical point of view based on the theories related to momentum moment theorem and momentum moment conservation law. Then, the dynamics model of the torque balancing mechanism is built, and the dynamics simulation analysis is carried out in this paper. The analysis results show that compared with the first-generation torque balancing mechanism, the residual torque of the second-generation torque balancing mechanism is reduced by 66.67%, the peak value of the residual torque is reduced by 57.55%, the mass of the balancing flywheel is reduced by 74.14%, and the torque balancing time is reduced by 42.86%. Finally, two torque balancing mechanism prototypes were fabricated at equal scale for test verification in this paper. The test results show that compared with the first-generation torque balancing mechanism, the residual torque of the second-generation test prototype is reduced by 40%, the peak of the residual torque is reduced by 25%, the mass of the balancing flywheel is reduced by 60.34%, and the torque balancing time is reduced by 51.06%. There are some differences between the simulation analysis and the experimental results, but the overall trend is consistent with the theory. Through theoretical derivation, simulation analysis and experimental verification, the correctness and feasibility of the proposed second-generation torque balancing mechanism are fully confirmed, which has certain reference significance and engineering application value for the torque self-balancing scheme of rotary tables. Full article

Due to the lack of comparative studies between discontinuous pulse-width modulation and model predictive control methods for reducing switching losses in two-level three-phase voltage source inverter, a comparative analysis of a generalized discontinuous pulse-width modulation and two model predictive control approaches for reducing switching losses is studied in this paper. Both generalized discontinuous pulse-width modulation and two model predictive control approaches are described and conducted in the simulation and experiment. The output performance is obtained by these methods after conducting in various conditions, including switching frequency, output power, and load conditions. It is validated that the generalized discontinuous pulse-width modulation control scheme achieves a better control performance at steady-state, while two model predictive control schemes have better transient-state performance with a superior dynamic. Additionally, the generalized discontinuous pulse-width modulation approach achieves better reducing switching losses performance and has slightly higher efficiency than that of two model predictive control approaches. Full article

In this paper, a time-varying formation tracking protocol for second-order multi-sgent systems (MASs) is presented. The time-varying formation considers translation, rotation, and scaling of the geometric pattern that defines the formation. The control law is simple yet effective, and it is composed of a trajectory tracking control and a consensus control that considers the position and velocity feedback of the connected agents in the MAS. The closed-loop system is asymptotically stable, and this was proved using the Gershgoring’s disk theorem. The performance of the protocol was extensively tested in experiments using a dynamic extension of the differential-drive robot model. The protocol was tested for different communication topologies and also dealt with switching topologies. The proposed protocol presented good performance regaring both time-varying formation and topology changes. Moreover, a comparison with an existing controller and with only trajectory tracking control has been provided, thus showing that the proposed protocol preserves the formation for all the tested topologies in a better way. Full article

The diagnosis of misalignment plays a crucial role in the area of maintenance and repair since misalignment can lead to expensive downtime. To address this issue, several solutions have been developed, and both offline and online approaches are available. However, online strategies using a small number of sensors show a higher false positive rate than other approaches. The problem is a lack of knowledge regarding the interrelations of a fault, disturbances during the diagnosis process, and capable features and feature vectors. Knowledge discovery in database is a framework that allows extracting the missing knowledge. For technical systems, optimal results were achieved by aligning (partially) automated experiments with a data mining strategy, in this case classification. The results yield a greater understanding of the interrelations regarding parallel misalignment, i.e., feature vectors that show good results also with varying load and realistic fault levels. Moreover, the test data confirm a specificity (range 0 to 1) for classification between 0.87 and 1 with the found feature vectors. For angular misalignment, potential vectors were identified, but these need further validation with a modified experiment in future work. For the study, two induction motors with 1.1 kW and 7.5 kW were considered. Furthermore, the findings were compared with additional motors of the same rated power. The findings of this work can help to improve the implementation of sensorless diagnostics on machines and advance the research in this field. Full article

In this study, a concept and design of a self-powered sensor that utilizes a triboelectric effect to evaluate the condition of tensile bolted joints was proposed. Based on the fact that the triboelectric charge yields electrostatic voltage induced by the separation of the contacting rough surfaces, the proposed sensor is a film-shaped triboelectric sensor made of inexpensive materials being installed between the objects to be fastened. The principle of the sensor is that it detects microscale relative motions between the contacting surfaces against an external vibratory load when the integrity of the fastened joint is compromised due to a decrease in the bolt’s fastening force. In this study, we designed and fabricated triboelectric sensor and tested it on a tensile bolted joint specimen subjected to inertial vibratory loading, and it was experimentally shown that the output voltage amplitude of the sensor increased as the bolt’s fastening force decreased. In addition, a modeling study was performed to explain the unexpected decrease in voltage amplitude observed at medium preloads, by combining the triboelectric and mechanical models with the experimental results of two different external circuit configurations. Estimation of the triboelectric charge density at the contacting surfaces was performed, which was found to be consistent with the contact mechanics model assumed. Finally, the calculation of the sensor output voltage based on the presented mechanical/triboelectric model was provided, confirming the validity of the modeling study. Full article

The paper proposes a new three-dimensional flexible hinge formed of several serially linked straight- and circular-axis segments that are disposed of in two layers. The novel hinge configuration is capable of large displacements and can be implemented in precision-compliant mechanisms that need to cover large spatial workspaces. Based on simplified geometry, an analytical compliance model is formulated that connects the loads to the displacements at one end of the hinge. Finite element simulation and experimental prototype testing of actual-geometry hinge configurations confirm the analytical model predictions. A related compliance-based analytical model evaluates the maximum loads that can be applied to the hinge and the resulting displacements. The two small-deformation analytical models are subsequently utilized to investigate the relationship between geometric parameters and the hinge performance qualifiers. Full article

This paper proposes a stator core design to achieve variable-flux characteristics enhanced by magnetic saturation in fractional-slot concentrated winding (FSCW) permanent-magnet (PM) motors without additional excitation systems, complicated structures, or active controls. Magnetic saturation induced at suitable locations can enhance variable-flux characteristics because the PM flux linkage is variable with the magnetic saturation in cores. In distributed winding configurations, magnetic saturation is induced symmetrically for each rotor pole by the fundamental that is dominant in the spatial distribution of the air-gap flux density. The magnetic saturation at each rotor pole can enhance variable-flux characteristics. In FSCW configurations, magnetic saturation is not induced symmetrically for each rotor pole because of several dominant space harmonics. The magnetic saturation at several rotor poles cannot enhance variable-flux characteristics. In the proposed design, the stator core has thin tooth tips for inducing magnetic saturation. The magnetic saturation in the stator core achieves the variable-flux characteristics that are effective for expanding the operating region or decreasing electromotive force and increasing torque. The efficacy of the proposed design is determined by estimating motor performance through a finite element method analysis and demonstrating the actual motor performance of a prototype. Full article

Machining hard-to-cut materials, such as cobalt (Co)-based superalloys, is a common problem in manufacturing industries. Background: wire electrical discharge machining (W-EDM) is one of the widely used cutting processes that causes layer (white layer—WL and heat-affected zone—HAZ) formation, and microcracks on the material’s surface. Purpose: this study investigates the effects of WL and HAZ on the electrochemical grooving (EC grooving) performance of Co-based superalloys. Two different surface types (W-EDMed and VFed) were used in the experiments. Result: the experiments showed that material removal rate (MRR) values increased up to 212.49% and 122.23% for vibratory finished (VFed) and wire-electrical-discharge-machined (W-EDMed) surfaces, respectively. Conclusion: This result indicates the presence of HAZ and WL that prevent current transition between two electrodes. However, increased voltage causes an increase in surface roughness, with increment rates at 71.13% and 36.08% for VFed and W-EDMed surfaces, respectively. Moreover, for the VFed surfaces, the groove lost its flatness at the bottom after an approximately 100 µm depth due to the different electrochemical machineabilities of HAZ and real surface texture. This result can be attributed to the different microstructures (HAZ and surface texture) showing different electrochemical dissolution rates. Therefore, high-depth distance HAZ and WL must be removed from the workpiece. Full article

Curvic couplings are used in applications demanding high positional accuracy and high torque transmission; therefore, improving their design and enhancing their load-carrying capacity is crucial. This study introduced the kinematic model Curvic3D, which was developed to produce the accurate geometry of both members of a curvic coupling using a CAD system. The model enabled the complete parametrization and customization of the coupling design using important geometric parameters. The couplings produced using Curvic3D were then imported into a finite element analysis model also developed as part of this study. A detailed analysis of the stresses developed on the teeth of the concave and convex parts provided information about the behavior of the coupling under different loading conditions. Finally, a series of geometric parameters, such as the number of teeth, the number of half pitches, the root fillet radius, and gable angle were examined as to their influence on the load-carrying capacity of the curvic coupling. The study concluded that all the examined parameters have a significant effect on the tooth flank and root area stresses. Full article

In metal-cutting technology, milling plays an important role in the product development cycle. The accurate modeling and prediction of milling forces have always been research hotspots in this field. The mechanical model based on unit-cutting force coefficients has a high prediction accuracy of cutting forces, and it is therefore widely used in the modeling of milling forces. The calibration of the cutting force coefficients can be realized by linear regression analysis of the measured average milling forces, but it needs to carry out multiple groups of variable feed slot milling experiments with full radial depth of cut, and it cannot well represent the interaction condition in peripheral milling with a non-full radial depth of cut. In peripheral milling, the tool will inevitably deform under the influence of cutting force in the direction perpendicular to the machining surface. If the force-induced deformation is ignored, the calibration of the cutting force coefficients will be out of alignment. For this non-slot milling condition where one side of the tool is mainly stressed, a cyclic calibration method for milling force coefficients considering elastic deformation along the axial contact range is proposed. Firstly, the cutting force coefficients are preliminarily calibrated by the experimental data, and, secondly, tool deformation is calculated through a preliminarily calibrated cutting force model cycle until convergence, before cutting boundaries are updated. The cutting force coefficient is then calibrated again, and it is brought back to the cantilever beam model in order to calculate the tool deformation again. The above process is repeated until the cutting force coefficient is convergent. Finally, the cutting force coefficients are obtained in order to predict and model the milling forces. Full article

Taking into account the issues faced by self-driving vehicles in multilane expressway scenarios, a lane-change decision planning framework that considers two adjacent lanes is proposed. Based on this framework, the lateral stability of an autonomous vehicle under near-limit conditions during lane change is studied by the phase-plane method. Firstly, a state-machine-based driving logic is designed and a decision method is proposed to design the lane-change intention based on the surrounding traffic information and to consider the influence of the motion state of other vehicles in the adjacent lanes on the self-driving vehicle. In order to realize adaptive cruising under the full working conditions of the vehicle, a safety distance model is established for different driving speeds and switching strategies for fixed-speed cruising, following driving, and emergency braking are developed. Secondly, for the trajectory planning problem, a lane-change trajectory based on a quintuple polynomial optimization method is proposed. Then, the vehicle lateral stability boundary is investigated; the stability boundary and rollover boundary are incorporated into the designed path-tracking controller to improve the tracking accuracy while enhancing the rollover prevention capability. Finally, a simulation analysis is carried out through a joint simulation platform; the simulation results show that the proposed method can ensure the driving safety of autonomous vehicles in a multilane scenario. Full article

The chain drive system, being one of the core subsystems of the scraper conveyor, greatly influences the coal-conveying efficiency due to its dynamic characteristics. Therefore, this paper employed an experimental method to compare the dynamic characteristics of the chain rings at various positions within the chain drive system. This article simulated the three most common working conditions. The results indicated that the chain speed, load, and surface undulation had a stimulating effect on chain vibration, with the most significant impact observed on the chain ring located in the middle of the adjacent scraper. The scraper exerted an inhibitory effect on the vibration of the chain ring. Among the factors studied, the load had the most significant influence on the transverse direction. When there was surface undulation, the vibration of the chain ring in the running direction formed an ‘N’ shape. This condition intensified the vibration during uphill and downhill stages while suppressing vibration at the slope peak. This research holds great significance in enhancing operation stability. Full article

The aging population has drastically increased in the past two decades, stimulating the development of devices for healthcare and medical purposes. As one of the leading potential risks, the injuries caused by accidental falls at home are hazardous to the health (and even lifespan) of elderly people. In this paper, an improved YOLOv5s algorithm is proposed, aiming to improve the efficiency and accuracy of lightweight fall detection via the following modifications that elevate its accuracy and speed: first, a k-means++ clustering algorithm was applied to increase the accuracy of the anchor boxes; the backbone network was replaced with a lightweight ShuffleNetV2 network to embed simplified devices with limited computing ability; an SE attention mechanism module was added to the last layer of the backbone to improve the feature extraction capability; the GIOU loss function was replaced by a SIOU loss function to increase the accuracy of detection and the training speed. The results of testing show that the mAP of the improved algorithm was improved by 3.5%, the model size was reduced by 75%, and the time consumed for computation was reduced by 79.4% compared with the conventional YOLOv5s. The algorithm proposed in this paper has higher detection accuracy and detection speed. It is suitable for deployment in embedded devices with limited performance and with lower cost. Full article