Cable-driven robots have attracted significant attention from researchers due to their advantages of low inertia, light weight, and extensive operational range. However, the inherent flexibility of cables and their unidirectional force transmission characteristics pose challenges for precise control. Achieving efficient and accurate motion control requires in-depth research on cable tension distribution, robot dynamics, and control strategies. This research reviews the research progress in the field of cable-driven robots. Firstly, it focuses on tension computation and optimization methods, including null-space method, geometric method, and least-squares method, comparing their advantages, disadvantages, and applicable scenarios. Secondly, it summarizes advancements in dynamic modeling approaches, such as the Lagrange method, Newton-Euler method, and the principle of virtual work, evaluating their strengths and weaknesses in modeling the dynamics of cable-driven continuum robots. Thirdly, it reviews the progress in control strategies for cable-driven robots, comparing model-based and model-free control approaches. Finally, the current state of research is summarized, and future development trends in cable-driven robots are discussed.

With the rapid development of generative artificial intelligence, the field of mechanical design has ushered in new changes. The design concept is gradually developed from the traditional “computer-aided + artificial experience” to “historical design data and knowledge + generative modeling” with advanced intelligence, and specific design behavior is developed from “manual modeling” to “generative modeling”, and the mechanical product design driver is developed from manual experience to data knowledge. In response to this development trend, a new mechanical design concept is proposed: Intelligent generative design (IGD). The content composition, core operation mechanism, design features, and key technologies of IGD are described in this article. On this basis, this study explores the application value of IGD in mechanical product design, and points out the new trend and development direction for the design of mechanical products.

The development trend of high thrust to weight ratio in aviation engines has driven the demand for high-quality machining of turbine blade film cooling holes. On the one hand, ultrafast laser has excellent performance in micro hole machining such as no recast layer and low hole wall roughness due to its approximate “cold machining” properties, and on the other hand, it is widely used in micro and nano machining due to its characteristics of no material selectivity, non-contact, and flexible machining. Therefore, ultrafast laser machining of film cooling holes has become a research hotspot. However, as a typical thin-walled cavity type component, the problem of ablation damage caused by laser penetrating the blade and irradiating the back wall is always difficult to avoid, and the back wall protection technology is also increasingly valued. This article is based on the ultrafast laser processing of film cooling holes. Firstly, it summarizes the ablation mechanism and simulation cases of ultrafast laser micro hole processing, and points out that simulation and experimental comparison constitute the general research route of laser processing; Furthermore, the process routes and specific processing effects of ultrafast laser film cooling hole processing are compared from three aspects: laser parameters, scanning methods, and processing steps; On this basis, the important role of key technologies such as material filling and process control in the processing of high-quality film cooling holes for back wall protection is further summarized. The performance requirements for filling, extraction, and protection of filling materials are clarified, and the feasible ideas and auxiliary positions of process control are determined. Finally, the key goals of ultrafast laser processing research and the systematic and collaborative development trend of back wall protection are pointed out, laying a foundation for the actual processing of film cooling holes.

In view of the design goals of lightweight, compact and high torque density of new motors, the printed circuit board (PCB) technology was introduced into the stator winding manufacturing process, and an axial flux double PCB stator motor is designed. Taking a single effective conductor bar as the research object, an analytical model of the motor's induced electromotive force, electromagnetic torque and output power was established. A finite element simulation model of the PCB motor was constructed, and key parameter optimization research was carried out. The electromagnetic field and temperature field simulation analysis of the series and parallel double PCB stator motors were carried out. Based on the constructed test platform, the output performance and temperature rise characteristics of the PCB motor under different configurations were studied. Under the rated operating conditions of 1 500 r/min and 3 A, the output torque of the series-connected double PCB stator motor is 130 mN·m, the torque density can reach 2 000 N·m/m³, and the measured maximum temperature is 180.6 ℃; the output torque of the parallel-connected double PCB stator motor is 103 mN·m, the torque density can reach 1 584.62 N·m/m³, and the maximum measured temperature is 110.5 ℃. Compared with the series configuration under the same working conditions, the temperature rise is smaller, which can effectively improve the PCB motor heating problem.

The harmonic reducer, a crucial component of industrial robots, works in complex and variable environments, leading to significant losses when failures occur. Due to the challenges in acquiring actual vibration data of harmonic reducers, the limited number of fault sample, missing data labels, and differences in data distribution under varying working conditions, a fault diagnosis method for harmonic reducer under different working conditions based on digital twin is proposed. Firstly, a digital twin model of the faulty harmonic reducer is constructed using dynamic modeling to generate twin data. Secondly, a virtual-real mapping method based on a cyclic generative adversarial network is proposed to achieve the mapping between twin data and real measured data. To enhance feature extraction and suppress noise interference, an improved semi-soft threshold function is integrated into a deep residual shrinkage network. Meanwhile, the extracted features are subjected to domain adaptation in unsupervised scenarios, using the maximum mean discrepancy to reduce distribution differences between domains, thereby achieving fault diagnosis under different working conditions. Finally, a fault simulation test bench for the harmonic reducer is established, and experimental verification shows that the proposed method achieves an average accuracy of 99.2% in all transfer tasks. It effectively addresses the fault diagnosis challenges of harmonic reducers in unsupervised scenarios under different working conditions.

The modeling analysis and identification of the spatial errors of CNC machine tools have always been important steps in error compensation. Firstly, the research history and technological development of the modeling theories and identification methods for machine tool spatial errors are discussed. Secondly, the modeling and analysis of machine tool spatial errors is an important prerequisite for error compensation. The modeling theory of machine tool spatial errors has been comprehensively reviewed and analyzed, including methods such as rigid body kinematics theory, homogeneous coordinate change theory, D-H transformation theory, multi-body theory, and screw theory. Thirdly, the accurate measurement and precise identification of spatial error elements in machine tools are key to achieving effective control. The current status and development trends of key measurement and identification methods for machine tool spatial errors are specifically introduced and comprehensively evaluated, including laser interferometer multi line method, body diagonal method, as well as ball bar method, laser tracker method, and other methods. Finally, the modeling, detection, and identification of spatial errors in integrated machine tools are systematically analyzed to identify the problems that still need to be solved in improving the spatial accuracy of existing CNC machine tools. The importance of technological innovation in improving measurement efficiency and accuracy is emphasized; And prospects for future development directions have certain guiding significance for improving the accuracy of CNC machine tools.

Micro coaxial unmanned aerial vehicles perform well in various complex environments due to their unique structure and performance advantages, especially in executing tasks with high complexity and confined spaces, such as military reconnaissance, disaster rescue, and other fields. Therefore, the research status and progress of the control mechanism and flight control algorithm of micro high-mobility coaxial UAV are reviewed. In terms of the manipulation mechanism, the attitude adjustment principle and design characteristics of the manipulation mechanism, such as tilt disk, center of gravity offset, lower rudder blade, motor cycle control and electromagnetic coil drive, are introduced, and their size parameters and main characteristics are compared. In terms of flight control algorithms, the principles and applications of traditional control methods and advanced control methods are expounded. Finally, the development characteristics and future trends of micro-UAV are analyzed, and the future trends are prospected, and it is pointed out that the integration and cooperation of multiple manipulation systems will be the future development direction to meet the increasingly diverse and complex task requirements.

In the process of numerical control programming for complex structural components, the difficulty in reusing machining process knowledge arises due to the heterogeneity of knowledge sources and the complexity of interconnections between knowledge. A knowledge recommendation method for structural parts machining process based on a large language model is proposed. By selecting and fine-tuning the large language model, a vertical domain model of machining process knowledge recommendation for structural parts is established. The evaluation results indicate that the model can recommend corresponding machining processes based on specific part features. To solve the problem of the model not being able to obtain the latest professional knowledge and the low accuracy of machining process recommendations, the LangChain application framework combined with a knowledge base is used to enhance the knowledge retrieval of the domain model and construct a process knowledge question answering system. Through corresponding indicator evaluation, the F1 value of the question answering system improves by 0.026 on the basis of the original domain model, and the accuracy of machining process recommendations is above 90%. In the process decision-making application of CNC programming for aviation structural components, this method recommends corresponding process knowledge based on part features. Compared with the automatic CNC programming system that does not use the method in this article, the efficiency of generating CNC codes for frame type structural components improves to a certain extent, which is of great significance for improving the decision-making efficiency of CNC programmers.

Vehicle dynamics theory is fundamental to automotive design and control. With the rapid development of automotive electrification and intelligence, novel chassis configurations characterized by distribution, modularity, and redundancy have disrupted traditional boundaries of vehicle motion functions. The integration of onboard, roadside, and connected intelligent sensing information has transformed vehicle systems into cyber-physical systems. Existing vehicle dynamics theories, however, struggle to uniformly characterize the dynamics of multi-mode chassis structures and fail to elucidate the mechanical interactions between vehicles and multi-source external environmental information, highlighting critical limitations in model generality and environmental information integration. To address these issues, a generalized vehicle system dynamics framework is proposed. This framework abstracts chassis constraints, inter-vehicle interactions, and information exchange as generalized internal forces within the vehicle system, thereby constructing a coupled dynamics system encompassing mechanical, electronic, and informational multiphysics interactions. Furthermore, it enriches the traditional “modeling-estimation-control” theoretical paradigm, forming a unified theoretical framework to guide the chassis design and coordinated dynamic control of high-performance vehicles.

Segment assembly is an essential process in shield construction. The artificial manipulation is ineffective, high-risk and irregular in its quality. It is of great significance to automate the segment assembly process for improving the construction quality of shield tunnel, increasing the operating efficiency and promoting the intelligent construction of underground engineering. Based on the research work carried out by domestic and foreign scholars in the automatic assembling of segments，the article summarizes the research work from four aspects：including auto-selection, automatic perception, automatic movement of assembly machines, and automatic servo system of assembly machines. The article analyzes the research progress and shortcomings of the key technologies in various aspects of automatic segment assembly. The purpose of segment selection is divided into design stage typesetting and assembly point selection in construction period. Assembly point selection mainly uses the segment axis to fit shield machine attitude. The parameters include gap of shield tail and stroke difference of propulsion cylinder, but their weight coefficient determination is strongly dependent on construction experience. Pose perception of segment method is divided into contact measurement and non-contact measurement. The image-based target detection technology in non-contact measurement is better, but its algorithm accuracy and efficiency still need to be improved. The D-H method is mainly used to describe the pose and motion of the mechanical arm. The trajectory planning focuses on using polynomial curves to smooth the motion path to reduce the abrasion of the machine joints. The segment assembly machine is developing towards the direction of parallel mechanism with redundant degrees of freedom, and its assembly efficiency and accuracy are better. The servo system of the assembly machine controlled by the proportional valve has high accuracy, and multi-axis motion can improve the efficiency of segment assembly. Finally, the deficiencies of research are discussed, and new insights and directions are proposed. The research can provide reference for further improvement of the automatic assembly technology of segments and promotion of the intellectualization of underground engineering equipment.

With the rapid development of digital manufacturing technology, a large number of machining process instances have accumulated in enterprise databases. Based on the basic principle that “geometric similarity likely leads to process similarity”, effective reuse of process knowledge can be achieved through identifying and extracting similar three-dimensional geometric process information, thereby enhancing the intelligence level of process decision-making systems and significantly shortening product development cycles. Against the background of rapid development in NC machining process reuse technology, systematically grasping its current status and future trends and providing comprehensive literature reviews for process designers has important theoretical and practical significance. The research systematically analyzes and summarizes the latest research progress of NC machining process reuse technology from three dimensions: first, at the macro process reuse level, methods for reusing the overall processing route of products are discussed; second, at the micro process reuse level, focus is placed on the precise extraction and application technology of process knowledge in specific processing links; finally, process reuse technology based on machine learning concentrates on the processing of unstructured CAD model data and the complex mapping relationship between them and process information. These research results not only have important theoretical guiding value for improving process design efficiency, but also show significant application prospects in promoting the improvement and optimization of enterprise process knowledge management systems.

The hypoid gear, as a complex spatial transmission widely utilized in aviation, specialized vehicles, and precision drive systems, has its meshing quality directly impacting the service performance of the entire machine. Although significant progress has been made in the design theory, generation mechanism, surface optimization, and manufacturing of this type of gear transmission, the increasingly performance requirements of high-level equipment present greater challenges for the active design of such transmissions. A detailed exposition of the research progress and development trends in the forward design methodologies of this type of transmission considering literature review, market research, and project studies has been provided. It focused on the configuration design, geometric parameter design, manufacturing parameter design, contact analysis, and tooth surface geometrical optimization of hypoid gears. Moreover, it systematically outlines the development trends of this transmission type to meet the service demands of high-level equipment and artificial intelligence. The aim is to provide theoretical and technical support for researchers and engineers in this field and to promote the advancement of hypoid gear forward design technology in China.

It is of great significance to efficiently scheduling various resources to complete tasks for intelligent workshops with multiple production factors. An efficient hybrid evolutionary algorithm is proposed to solve a flexible job shop scheduling problem with multiple production factors. Firstly, a MFFJSP-WA model incorporating four production factors—jobs, machines, AGVs, and workers—is constructed with the objective of minimizing the maximum completion time based on the analysis of the problem background and the operation conditions of multiple factors. Since the model includes four kinds of decision variables, the hybrid initialization strategy combining heuristic and random methods is proposed to generate a high-quality initial population. A global search method based on classical genetic operators is designed according to the four-layer encoding structure of individuals. To address the issue of easily falling into local optimum, a multi-neighborhood local search method guided by a memory mechanism is proposed to enhance the algorithm's local search capability. Finally, the proposed algorithm is tested on sets of instances expanded from benchmarks. The experimental results show that the hybrid initialization strategy and local search operation can effectively improve the algorithm’s performance. Compared with various advanced algorithms in the field, the proposed algorithm is superior in solution quality performance.

As one of the core executive unit of the multifunctional system of launch vehicles, the action reliability and separation accuracy of separation and thrust mechanisms directly affect the success or failure of space launch missions. With the increase of the complexity of space missions and the carrying capacity of rockets in various countries, separation and thrust mechanisms face more stringent technical requirements in terms of bearing capacity, response speed, and environmental adaptability. A review systematically combs through the application and development status of separation and thrust mechanisms for launch vehicles at home and abroad, introduces in detail the working principles and characteristics of various separation and thrust mechanisms from four aspects: pyrotechnic, spring, pneumatic, and other energy sources, reviews the development in the field of dynamic characteristics, impact response, and reliability of separation and thrust mechanisms for launch vehicles, and introduces the simulation analysis techniques of some typical mechanisms. Finally, it looks forward to the development trend of separation and thrust mechanism products for launch vehicles, aiming to provide references for the innovative design and systematic development of separation and thrust systems for new-generation launch vehicles.

The aircraft skin is the primary component forming the aerodynamic shape of an aircraft, characterized by large size, thin wall (thickness 2～6 mm) and complex structure. Currently, manufacturers generally adopt a manual comparison-marking-trimming method to remove the edge allowance of the skin parts, leading to large cumulative human errors and difficulties in controlling assembly quality. Vision/force-guided industrial robot milling with high-flexibility and large operation range provides a novel approach to solving these problems. However, difficulties in simultaneous calibration of dual-robot systems, smooth path generation for machining and accurate control of the robot’s trajectory have become the bottlenecks restricting the application of robot milling for the aircraft skin. The above challenges can be summarized as the simultaneous decoupling of spatial transformation and the quantitative control of pose errors. To address these issues, this paper conducts in-depth research on dual-robot system calibration, smoothing machining path generation and closed-loop feedback control of the robot’s end-effector. The Part I proposes simultaneous calibration method of dual-robot system for robotic tracking/measuring-machining, establishes kinematics model of robot-tracking system and studies method to generate a smooth machining path for aircraft skin. The Part II studies closed-loop feedback control model for robot’s end-effector under external tracking system, develops closed-loop feedback control system for robot. The simultaneous calibration accuracy test of dual-robot, the trajectory accuracy test of end pose with closed-loop control, and the robotics milling accuracy test of typical skin samples are carried out to validate the effectiveness of the proposed methods.

Structural reliability analysis is crucial for ensuring the safe and reliable operation of engineering equipment. The accuracy of traditional first-order reliability methods and their improved algorithms is limited by the type of limit state functions and gradient information，which may not be applicable to certain complex engineering cases. In the face of increasingly complex engineering scenarios，although the establishment strategies for multi-fidelity models have been widely studied，their application potential in reliability analysis remains to be further developed. Therefore，an adaptive Multi-Fidelity Kriging (MF-Kriging) model-assisted first-order reliability analysis method is proposed in this study. Firstly，the first-order reliability analysis problem is transformed into an unconstrained optimization problem through the augmented Lagrange function，enabling the use of heuristic optimization algorithms for its solution. Secondly，adaptive MF-Kriging modeling is employed by integrating multiple data sources to further reduce the computational cost of reliability analysis. Based on this reliability analysis method，any adaptive MF-Kriging modeling strategy，whether global or local search，can be nested. Additionally，considering that one of the important steps of the first-order reliability method is to accurately locate most probable point(MPP)，this study proposes a hybrid MF-Kriging modeling strategy. By balancing a global search strategy with a local search strategy based on iterative MPP neighborhood，it achieves a trade-off between local accuracy and global convergence in reliability analysis. Finally，the proposed method was applicated through four mathematical examples and three engineering cases. The results illustrate that the proposed method offers significant advantages in terms of accuracy，efficiency，and robustness.

Equipment maintenance is an important basis to ensure normal production, the existing intelligent maintenance technologies mainly rely on signal analysis, data mining or expert knowledge reuse. However, with the improvement of the automation and integration degree of production equipment, the relationships among the characteristic signals of various operating anomalies, multi-source causes and maintenance schemes present higher fuzziness and complexity, the integration analysis of signals, data and knowledge is the key to improve the accuracy and efficiency of equipment maintenance. Therefore, knowledge graph technology is used to integrate the ternary data of “human”, “cyber” and “physical” to support the abnormal diagnosis and maintenance scheme decision of complex equipment, improve the intelligent degree of equipment maintenance, and avoid the one-sidedness of decision. Firstly, the ternary man-machine object data in the field of equipment maintenance is defined and the ternary ontology design is completed to guide the construction of knowledge graph data layer. Secondly, preprocessing is conducted on the ternary data of human-cyber-physical and a unified joint entity and relation extraction model with mixed attention，MAREL is built to automatically extract knowledge from the ternary data of human-cyber-physical, and to establish associative relationships between them, thereby achieving the fusion of ternary human-cyber-physical data; MAREL dissolves the task into two related decoding modules to solve the entity overlap problem, and the mixed attention mechanism is used to enhance the long text processing capability of the model, the test on the Chinese data set SKE proves that MAREL has excellent performance. Finally, the construction of human-cyber-physical knowledge graph for the maintenance of robot equipment in an automobile production workshop is taken as an example, the effectiveness of the proposed method is verified, results show that the knowledge graph can effectively integrate the ternary data of “human”, “cyber” and “physical”, and provide decision support for intelligent equipment maintenance.

To address the motion instability issues of existing wall-climbing robots in internal/external right-angle transition scenarios, this study proposes a rigid-wheeled magnetic adhesion robot integrated with an auxiliary transition mechanism and investigates its multi-surface motion characteristics and transition mechanisms. First, a static mechanics model is established to derive mechanical equations under right-angle transition conditions, determining the critical adsorption force range for slip-free and anti-tipping failure. Through analysis of the robot’s right-angle transitional states, the influence of discontinuous geometric wall features on adsorption forces during internal/external right-angle transitions is revealed, and the required output torque conditions for the auxiliary mechanism during external transitions are derived. Subsequently, a multi-wheel group coordinated control strategy for internal/external transitions is designed. Furthermore, the functional relationship between air gap spacing and magnetic adhesion force is clarified through magnetic field simulations, and permanent magnet spatial layout parameters are optimized using contact mechanics theory. A prototype is developed and validated through multi-condition experiments, demonstrating that the designed robot exhibits excellent load capacity and wall adaptability. Compared to traditional wheeled structures, its internal/external transition performance is significantly improved, verifying the effectiveness of the structural design, mechanical analysis, and control methodology.

Lithium plating on the negative electrode is one of the critical issues restricting the safety and lifespan of lithium-ion batteries. To enhance the safety and extend the lifespan of lithium-ion batteries, a lithium plating diagnosis method is proposed which is based on multidimensional feature mining and cluster analysis. Low-temperature lithium plating experiments are designed, and experimental data of batteries are collected. A high-precision equivalent circuit model of the battery is established, and a lithium plating feature extraction method based on model parameter identification and capacity increment analysis, as well as a feature space dimension reduction method based on principal component analysis, are proposed. Based on this, an adaptive grading diagnosis method for lithium-ion battery lithium plating faults is proposed using a density-based clustering algorithm optimized by particle swarm optimization, and the accuracy of the proposed method is verified based on the difference in capacity before and after lithium plating and scanning physical detection methods. The diagnostic results show that the lithium plating diagnosis results based on multi-dimensional features are optimal. Compared with single-dimensional lithium plating diagnosis methods based on battery model features, the missed diagnosis rate decreases by 8.00%, and compared with single-dimensional lithium plating diagnosis methods based on capacity increment curve features, the missed diagnosis rate decreases by 8.00% and the misdiagnosis rate decreases by 3.63%. At the same time, scanning electron microscope and inductively coupled plasma inspection results are consistent with diagnostic results, and can accurately diagnose mild and severe lithium plating, realizing graded diagnosis of lithium plating in lithium-ion batteries.

In order to solve the problem of tooth flank accuracy error that exists when grinding helical gears with worm wheel for topological modification, a topological modification method based on flexible electronic gearbox is proposed. First of all, according to the forming principle of involute tooth flank and topological modification curve, the mathematical models of standard involute tooth flank and double drum modification tooth flank are established. Secondly, the kinematic inverse solution method is applied to derive the additional motions of each axis corresponding to the topological modification tooth flank. Since the multi-axis linkage synchronization control of machine tool in the process of generating gear grinding is realized by the control of electronic gearbox, the additional motion amount is proposed to be added in the control model of electronic gearbox. Finally, numerical simulation of tooth flank modification is carried out to compare the tooth flank deviation obtained by traditional modification method and the tooth flank modification method based on flexible electronic gearbox through two numerical simulation examples. The results show that this method can effectively improve the tooth flank modification accuracy of gear grinding with worm wheel.

In-space additive manufacturing (ISAM) is considered a key technology for achieving deep space exploration, efficient utilization of space resources and long-term habitation on extraterrestrial bodies. When compared to conventional terrestrial additive manufacturing, the development of ISAM technology is uniquely challenged by the space environment, which is characterized by high vacuum, intense radiation, microgravity and extreme temperature variations. Breakthroughs are required in equipment compatibility, process control, raw material selection and other aspects. ISAM research is currently focused on two cutting-edge fields: On-orbit manufacturing and surface construction on extraterrestrial bodies. Various types of forming processes, including materialextrusion, directed energy deposition, powder bed fusion, stereolithography and computational axial lithography, are being developed. The current research status and development trends of ISAM technology are reviewed, with comparisons made between the process principles and characteristics of various ISAM technologies. Typical microgravity verification platforms and their testing methods are summarized. Cases of ISAM technology research and application in actual space environments are outlined. The significant challenges and potential opportunities faced in the development of ISAM technology are analyzed and key directions for future research are anticipated.

As an effective solution for structural vibration suppression, the breakthrough in performance bottlenecks of dynamic vibration absorbers (DVAs) holds significant strategic importance for enhancing the reliability of high-end equipment in national strategic sectors such as aerospace and defense. The research progress in five major DVA types is systematically reviewed: single-degree-of-freedom (SDOF) DVAs, multiple DVAs, multi-DOF DVAs, tunable DVAs, and nonlinear DVAs, with a focus on structural innovation. SDOF DVAs, characterized by their structural simplicity, stability, and easy implementation, remain the most widely used configuration in engineering, nevertheless their narrowband limitations have spurred the development of combined and multi-DOF designs. Multiple SDOF DVAs achieve broad bandwidth through parallel/serial topological configurations, balancing bandwidth enhancement with engineering feasibility. Multi-DOF DVAs leverage spatial freedom of mass units to enable efficient multi-dimensional or multi-mode vibration suppression. Tunable DVAs integrate tuning mechanisms with semi-active control to address optimal adaptation under time-varying structural dynamics. Nonlinear DVAs demonstrate unique advantages in broadband vibration control via targeted energy transfer mechanisms. Comparative analysis reveals that structural innovations, including freedom-degree expansion, parameter adaptive tuning, and nonlinear stiffness design, have substantially improved vibration suppression performance and environmental adaptability, driving a paradigm shift from traditional parameter optimization to configuration-driven design. Simultaneously, the reconfiguration of stiffness units based on flexures has established a theoretical cornerstone for configuration-driven performance enhancement of DVAs. Future advancements are expected to achieve higher vibration attenuation amplitudes, superior dynamic adaptability, broader suppression bandwidths, and multi-directional vibration control. Furthermore, this field is poised to catalyze the evolution of integrated vibration suppression, energy harvesting and sensing technologies, providing theoretical foundations and technical frameworks for vibration control in aerospace and advanced manufacturing systems.

Hydraulic excavator mainly adopts the centralized drive mode of single power source for multi-actuator, which has the problems of large throttle loss and low utilization rate of potential energy, resulting in low average efficiency. The distributed electro-hydraulic system adopts independent variable speed control, eliminates the throttle loss and recovers the energy under assistive conditions, which is an effective solution to improve the efficiency. Therefore, a self-contained electro-hydraulic actuator (EHA) is designed to offer high power, high speed and four-quadrant operation capabilities, which is integrated into the 6t excavator boom to conduct a comprehensive performance test and analyze the energy efficiency and loss distribution. During the cycle of boom raising and lowing, the driving efficiency of EHA is up to 61.2% and the energy recuperation efficiency of EHA is up to 53.3%. In terms of loss distribution, the loss of motor-pump accounts for more than 80% of the total loss of EHA, and the efficiency optimization of motor-pump is very important. This study realizes the application of the EHA in the excavator, and provides an effective solution to improve the energy efficiency of the electro-hydraulic control system of the mobile machinery.

With the wide application of pulse width modulation technology and the rapid increase of bus voltage of variable frequency motor, the shaft current damage of high power variable frequency motor has become the dominant factor of bearing failure in wind turbine, rail transit traction motor and other fields. Aiming at the problem of evaluating the micro-damage probability of bearing current in high-power variable frequency motor, considering the influence of micro-roughness of bearing raceway, the calculation method of oil film partial pressure and withstand voltage is proposed, and the EDM breakdown probability model of bearing oil film in variable frequency motor is constructed. The oil film breakdown probability and shaft current density of the non-drive end bearing of 190 kW traction motor are calculated analytically. The modeling results are similar to the shaft current damage in the actual service of the bearing, which verifies the validity and reliability of the model. The results show that the shaft current damage is usually concentrated at the maximum load ball in the Hertz contact area of the inner ring or the outer ring of the bearing, and the possibility of breakdown discharge of the oil film is the highest. The insulating bearing can effectively suppress the breakdown probability of the oil film and the shaft current density by relying on the small capacitance partial pressure and large resistance blocking characteristics of the coating.

Multistable characteristics represent a unique nonlinear mechanical phenomenon. When a system exhibits multistable characteristic, it means that there are multiple stable equilibrium states (states where the energy reaches a local minimum). In these states, even if the system is subjected to small external disturbances, it can maintain its state by itself without energy input. Multistability can exhibit a variety of mechanical characteristics/behaviors, such as negative stiffness, self-balancing, and disturbance resistance, etc. It has gradually shown great application value in fields such as aerospace, smart robot, and biomedicine, and has attracted a large number of researchers in recent years. This research reviews the typical research achievements on multistable mechanisms and structures in recent years. First, the principles, configurations, and design methods of different multistable mechanisms/structures are summarized from the aspects of compliant mechanisms, origami structures, kirigami structures, soft materials, and tensegrity structures; Then, the typical applications of multistable mechanisms/structures in various application scenarios are introduced; Finally, it offers insights into future development trends, aiming to provide new perspectives for enhancing the design and application of multistable mechanisms and structures.

Data-driven models have been widely used to predict the fatigue life of additive manufactured materials because of their powerful capability in high-dimensional nonlinear modeling. However, owing to the high requirement of data support, these models are not economically feasible for many practical cases. To tackle this issue, a transfer learning-based model for fatigue life prediction of additive manufactured materials is proposed, through combining source and target domain data and introducing a clustering-based procedure for hyper parameter optimization. The proposed model can collaboratively model fatigue life using experimental data from different manufacturing conditions, effectively addressing the insufficiency of training data under a single condition and the inconsistency of data distributions under multiple conditions. Experimental results of 316L stainless steel processed by laser powder bed fusion are collected for model verification. The results show that, compared to several degradation models, the proposed model has better prediction performance and lower data requirement, exhibiting promising potential in predicting the fatigue life of additive manufactured materials.

Manufacturers are now faced with the challenge of responding quickly to diverse markets and uncertain demands. Since there are more and more processing tasks for small batches of personalized products, the traditional tool condition monitoring technology and tool management mode is no longer applicable to the manufacturing environment with frequent changes in working conditions. Existing literature review on tool condition monitoring and remaining life prediction mostly focuses on a single fixed working condition. The latest achievements on tool condition monitoring and remaining life prediction under complex working conditions at home and abroad are systematically analyzed and summarized to bridge above limitations, and the further research direction is put forward. According to the progress of domestic and foreign scholars in recent years, the advances of investigation on sensor-based indirect tool condition monitoring method are firstly discussed in detail. The advance on applications of the signal processing and feature extraction, monitoring model selection under complex working conditions are reviewed from the perspective of single-sensor monitoring and multi-sensor joint monitoring respectively. Next, the current status of the application of the transfer learning method in complex working conditions tool wear condition monitoring technology are summarized; Then, the development status of remaining life prediction under complex working conditions is summarized from wear degradation model, data-driven model and hybrid model; Finally, the focus and development trend of tool wear monitoring and residual life prediction under complex working conditions in the future are generalized. It has certain theoretical reference and enlightenment for future research work.

The hexapod external fixator is widely used in the treatment of fractures and the correction of skeletal deformities. Synchronized electrical adjustment of its six connecting rods proves advantageous in preventing harmful shear stresses, while measurement of mechanical load-sharing ratios enables monitoring of skeletal growth and healing progress, thereby avoiding bone-end stress shielding to facilitate recovery. An electromechanical hexapod external fixator system is accordingly developed, comprising three primary components: a structural frame, control module, and human-machine interface. An electromechanical structure incorporating built-in motors and pressure sensors is designed to enable automatic rod-length adjustment through control module operation and real-time force monitoring. A mathematical model of the fixator is established, and the fracture reduction algorithm and the calculation method for the axial load-sharing ratio of the fixator are derived, permitting determination of repositioning parameters and load distribution under various installation configurations and deformity conditions. A fixator-fracture simulation model is established to verify the correctness of the fracture reduction algorithm. A femoral fracture reduction experiment is conducted, and the results show that the electric hexapod external fixator system can effectively achieve fracture reduction and monitoring of the fixator’s force.

The new energy vehicles, exemplified by electric vehicles（EVs）, have embraced unprecedented opportunities for development. Lithium-ion batteries（LIBs）, leveraging their advantages such as high energy density, low self-discharge rate, and long lifespan, have emerged as the mainstream choice for EV power systems. However, the significant degradation of LIBs performance at low temperatures directly leads to reduced driving range, prolonged charging time, and potential safety hazards for EVs, posing a core challenge to their widespread adoption. Low-temperature heating, as one of the key methods to overcome the performance bottleneck of batteries at low temperatures, is currently the focus of industry research. This study comprehensively summarizes and discusses the latest advancements in low-temperature heating technologies for LIBs and their current application status in real vehicles, encompassing external heating, internal heating, and hybrid heating. It thoroughly elaborates on the principles, latest progress, strengths, weaknesses, and potential optimization opportunities of each technology. Additionally, it conducts a qualitative comparison of each technology and analyzes the current application status of heating technologies in real vehicles. Finally, the paper explores the future development prospects of low-temperature heating technologies, with an emphasis on key technological breakthroughs and opportunities, providing a comprehensive perspective for the next steps in research and real-vehicle applications of low-temperature heating technologies.

Harmonic drives are commonly employed in the joints of robotics, where they facilitate power transmission driven by servomotors. The flexible thin-walled bearing, an important component of harmonic drives, is susceptible to failures under the cyclic squeezing and alternative load conditions of cam, and it generates harmonics in the current of the connected motor. However, the operation conditions of robots can induce non-stationary changes in fault frequencies, coupled with the frequencies induced by the structural deformation of the flexible bearing, which makes the modulation effects of fault on current complicated. The stator current model for different faults in the flexible thin-walled bearing of harmonic drives is proposed. This model accounts for the effects of varying speed and load conditions, the frequencies due to variations of the long and short axes of the flexible thin-walled bearing, and the fault frequencies into the current model. By establishing a simulated experiment platform of harmonic drive with different faults, the tests under constant speed and load, constant speed varying load, variable speed constant load, and variable load variable speed conditions are conducted to validate the applicability and accuracy of the established current model considering fault on different part of the flexible thin walled bearing.

Molten salt possesses excellent thermophysical properties such as high heat capacity, thermal conductivity, and thermal stability, making it widely used as a heat storage medium in fields such as nuclear power and concentrated solar power generation. However, high-temperature molten salt exhibits severe corrosiveness towards metallic materials. Based on the evaluation and mechanism of molten salt corrosion of stainless steel, the main strategies to slow down the corrosion of molten salt on stainless steel are introduced from the aspects of molten salt modification and surface modification of materials. Firstly, the paper discusses the evaluation of molten salt modification methods such as thermal purification, chemical purification, and addition of nanoparticles to reduce metal corrosion. Subsequently, it analyzes the research progress on material protection methods, including aluminum alloy coatings, surface fractal textures, graphitization coatings, nanoparticle coatings, and aluminum-containing stainless steel pre-oxidation, and their effects on mitigating molten salt corrosion. Finally, it emphasizes the importance of developing aluminum-containing stainless steels for the safety design, manufacturing, and operation and maintenance of concentrated solar power systems.

In order to meet the development needs of large-scale, lightweight and high storage rate of space optical system sunshield structure, a large-scale special-shaped deployable sunshield structure is proposed. Firstly, according to the shading requirements of the optical system, the overall design scheme of the deployable sunshield structure is studied. Secondly, based on the Accordion origami principle, the folding scheme design of the membrane structure of sunshield is carried out, and a parametric mathematical model of irregular polygonal columnar folding structure is established. Thirdly, the ABAQUS simulation software is used to establish the simulation model of the membrane structure deployable process, and the variation law of the stress during the deployable process is analyzed. Finally, a scale-down prototype is developed, and the deployment function test is carried out. The simulation and experimental results show that the designed sunshield structure can complete the deployment action successfully, the membrane has no interference with the deployable support mechanism, and there is no jam in the deployable process, and the membrane does not tear. The research results can provide certain reference for the structural design and research of other spacecraft sunshield.

The processing channels of integral components such as blisk, integral impeller, and closed impeller of aero-engine are twisted, narrow, and deep, and are prone to various machining interference, which are typical difficult-to-machine components. To improve their machined surface quality, the robotic belt grinding process that is widely used as the last finishing process of the integral component is taken as the research object, and a multi-objective planning method of interference-free machining postures for robotic belt grinding of integral components is presented. With this method, a definition of interference-free robot configuration space (IFRC-Space) is first proposed. Next, an exploration experiment on the evolution law of IFRC-Space along the toolpath when grinding integral components is carried out, which concluded that IFRC-Space varies continuously along the toolpath. Based on the conclusion, a rapid computation method of IFRC-Space is proposed by using the edge detection operator of images. Then, a multi-objective optimization algorithm of grinding postures is advanced based on IFRC-Space. With the algorithm, the indicators including interference avoidance, singularity avoidance, smoothness of grinding postures, and the kinematic performance of the robot can be comprehensively considered, which enables the automatic generation and multi-objective optimization of the interference-free machining postures for robotic belt grinding of integral components. The effectiveness and practicability of the proposed method are verified by toolpath planning experiments for robotic belt grinding of aero-engine blisk and closed impeller.

Blade vibration and tip clearance are critical parameters reflecting the operational status of aero-engines, containing abundant fault and health information. Real-time monitoring and deep analysis enable fault diagnosis and early warning of engines. This article reviews contact-based measurement methods for blade vibration as well as non-contact measurement techniques such as blade tip timing and tip clearance monitoring. It summarizes important research achievements in related technologies domestically and internationally in recent years, focusing on three main aspects: Types of blade vibration and typical faults, monitoring and identification methods, and fault diagnosis and warning methodes. In particular, the application of these techniques in typical faults such as flutter, surge, and rubbing is emphasized. Finally, the future development trends of aero-engine fault diagnosis and warning technology based on tip monitoring are prospected from five perspectives: high-precision high-speed acquisition, mechanism and evolution path analysis, multi-source fusion testing, fault database optimization, and machine learning-enabled intelligent diagnosis.

The joints of robots often exhibit complex dynamic characteristics such as strong nonlinear stiffness and hysteresis, nonlinear friction, and kinematic transmission errors. These factors significantly impact the positioning accuracy and smoothness of joint movements. Therefore, analyzing joint errors and studying parameter identification methods are crucial. This research uses the PSO-LSSVM algorithm to obtain an accurate Preisach model for robot joints, describing their nonlinear stiffness and hysteresis characteristics. The FFT algorithm is employed to identify the high amplitude components in the kinematic transmission error model. The Levenberg-Marquardt algorithm is used to accurately obtain the Stribeck model, which describes the nonlinear friction characteristics of robot joints. Finally, based on the analysis results of the error models and identification algorithms, experiments were designed to verify the reliability of the models and identification methods. This research can provide references for the subsequent design of high-precision robot joint controllers.

In the context of monitoring big data, the acquisition of ideal data is often constrained, resulting in a scarcity of high-value data. Consequently, traditional intelligent prediction models are characterized by weak generalizability and low accuracy. To address this issue, a sequential contrastive attention network (SCAN) is proposed to effectively utilize fragmentary, non-ideal data without complete lifespan labels for predicting the remaining useful life of equipment. Initially, a sequential contrastive encoder is constructed to automatically annotate the deep degradation features of non-ideal data without requiring lifespan labels, enhancing the temporal autocorrelation of the extracted features. Subsequently, a lifespan prediction attention decoder is designed to dynamically allocate weights to the encoded degradation features and perform parallel computation fitting. On this basis, an encoding-decoding training optimization mode based on elastic weight sharing is introduced to achieve an organic combination and efficient interaction between the encoder and decoder. The effectiveness of the proposed method is verified using full lifespan test instances of bearings and urban rail transit train wheels. Results demonstrate that the proposed method achieves superior prediction accuracy compared to other approaches, with an average accuracy improvement of approximately 34% in both application scenarios.

The coarse diamond grinding wheel can realize the high-efficiency grinding processing of hard and brittle materials, but its grinding quality is poor, and the truing efficiency is extremely low. The ultrasonic assisted mechanical-chemical truing method is proposed to carry out high efficiency and precision truing for coarse diamond grinding wheel to obtain the truncated protrusive micro grain cutting edges, performing the high efficiency and precision mirror grinding processing of hard and brittle materials of silicon carbide ceramics. Ultrasonic assisted mechanical-chemical truing utilizes the ultrasonic high-frequency friction vibration between diamond and cast iron truing tools and atomic affinity chemical catalytic effect to rapidly remove graphited diamond to realize the efficient precision truing of coarse diamond grinding wheel. Firstly, the influences of ultrasonic truing process parameters including ultrasonic power, truing pressure and truing time on the protrusion homogeneity of #80 coarse diamond grains are investigated; then, the effects of the ultrasonic assisted mechanic-chemical truing parameters and grinding process parameters on the ground surface quality of silicon carbide ceramic are comparatively analyzed. The experimental results show that the ultrasonic assisted mechanic-chemical truing can effectively improve the grain protrusion homogeneity of grinding wheel, and when the ultrasound power, truing pressure, and truing time are 80%, 80 N, and 120 s, respectively, the percentage of the grains with protrusion height ranged from 96～135 μm increases from 69.8% (before truing) to 92.1%. Compared with the traditional mechanical truing, the diamond grinding wheel after ultrasonic assisted truing has better grinding performance and can reduce the surface roughness of silicon carbide by about 84.6%. When the rotational speed N=18 000 r/min, feed speed v_f=10 mm/min, grinding depth a_p=1 μm, the macroscopic ground surface of silicon carbide presents a mirror effect, and its surface roughness R_a can reach 46 nm.

Most existing intelligent fault diagnosis studies focus on improving accuracy, implying that decisions are made only by models. From the safety aspect, this over-reliance on models can lead to users having no way of knowing even if the model gives untrustworthy diagnostic results; from the ethical aspect, the current artificial intelligence (AI) technology lacks moral guidance, and the relevant laws are not yet perfect, so it is difficult to pursue responsibility in case of misdiagnosis. A reliable diagnosis model should not only provide as accurate results as possible, but should also point out the possibility of its decision failure to warn the user. Therefore, it is necessary to assess the confidence of the results to mitigate the risk of model failure and to achieve trustworthy fault diagnosis. However, modern deep learning models are often poorly calibrated, i.e., there is a mismatch between the softmax output, which is often considered to characterize the confidence of the result, and the true probability of the result being correct, leading to a significant bias in using it directly as a confidence level. To this end, we propose a calibration technique called adaptive confidence penalty that fine-tunes the strength of the confidence penalty applied to each training sample, which in turn affects the softmax probability of the validation/testing samples inferred by the model. The method compensates for the limitation of the original confidence penalty method that uses a fixed penalty strength without considering the confidence characteristics of each sample, further improving the calibration quality and obtaining well-calibrated diagnosis models. The experimental results illustrate the motivation for designing the proposed method and demonstrate its superiority.

Blade failure is one of the most serious failures of the gas turbine. Real-time monitoring and early warning of blade failure is an effective method to improve the operational reliability. Based on the casing vibration signals, this study explores the influence of blade conditions on the pressure and vibration of the casing of gas turbine, in order to solve the difficult problem of early warning and diagnosis of blade failures. The dynamic pressure in the wake flow caused by the normal and fault conditions of the rotating blades is firstly simulated based on the Fourier series superposition, and the transmission characteristics of the dynamic pressure in the casing are analyzed. Secondly, a simplified finite element model of the casing-stator-blade based on plane beam element is proposed and established, and the natural frequency and mode shapes of the casing are solved, and the vibration characteristics and response law of the casing under normal and fault wake pressure are analyzed at the same time. Based on the above theoretical, the blade early warning indicators and diagnostic process applicable to the failure of gas turbine are proposed. Finally, it is verified by two actual industrial blade failure cases of gas turbines. The results show that the wake pressure generated by the rotating blade mainly consists of multi-order high-frequency blade passing frequency components and rotational frequency components, and the amplitude of each order decreases with the increase of the order; blade failure causes the change of blade passing frequency amplitude in the casing vibration signal, and the more significant is the sidebands on both sides of the frequency with the rotational frequency as the interval, and these features are crucial for the identification of blade failure, which is the basis for the establishment of blade fault warningindicators. The results provide theoretical guidance for improving the operation reliability of the gas turbine.

As part of the work of the Advanced Chinese Anthropometry-based human digital models (AC-HUMs) project at China Automotive Engineering Research Institute, the aim is to use the geometric data of the 50th percentile of Chinese anthropometry to establish a finite element model of the human chest and abdomen with detailed anatomical structures. The key bone data is verified using the data from seven major regions, and the biofidelity of this model under various load conditions is validated by benchmarking it against the cadaver test data in the literature. The established human chest and abdomen model is based on Chinese human body data. The results show that this model can represent the biomechanical characteristics of Chinese males at the 50th percentile. The established finite element model of the human chest and abdomen has a high level of biofidelity and demonstrates high stability in simulation calculations under conventional impact loads. It can provide an effective tool for studying the characteristics of chest and abdomen injuries among Chinese traffic participants.