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.

As a representative of innovative energy storage devices, lithium-ion batteries have been widely used due to their excellent performance and environmentally friendly properties. However, the long charging time caused by slow charging and the degradation caused by fast charging remain critical issues that hinder the further promotion and development of lithium-ion batteries. To this end, the design of the fast charging strategies of lithium-ion batteries has become a hot research topic recently. To summarize the research progress, a systematic review of current research on this topic is presented from three aspects： formulation of the charging problem, establishment of battery models, and design of charging methods, all core elements in the fast charging strategy design. First, the research background of the fast charging strategy design is introduced. Specifically, how to set the optimization objectives, constraints, and design variables of the design problem is investigated. Second, the internal mechanisms of lithium-ion batteries and some commonly used battery models are briefly described, and the modeling methods that incorporate machine learning are also summarized. Additionally, various existing charging methods are especially analyzed and classified based on their characteristics. Moreover, based on the current research status, some future directions are given, aiming to offer researchers a valuable opportunity to design more efficient and user-friendly fast charging strategies.

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.

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.

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.

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.

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.

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.

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.

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 bionic leg landing gear system based on multilink configuration was designed to solve the problems of low intelligence and poor adaptability of traditional unmanned helicopter landing gear systems when landing on complex terrains, as an effective supplement to the original fixed landing gear. Based on the analysis of application scenario requirements, this article provides the design concept and overall configuration of a bionic leg landing gear, and conducts research on system integration and verification technology on this basis. Firstly, based on the six-leg design scheme, the structural design and drive/control system design method of the bionic leg landing gear are given. Then, for a certain type of unmanned helicopter for verification, a physical prototype of the bionic leg landing gear was constructed, and the collaborative control and fusion design method of the flight control-leg control-terrain recognition system was proposed. Finally, based on this prototype, the tests that the whole machine vibration characteristics test and ground resonance analysis, carrying capacity test in laboratory, field flight landing verification are completed. These researches indicate that, the multilink bionic leg landing gear can achieve a lightweight design that accounts for no more than 25% of the maximum takeoff weight, and can land on unstructured terrain with no more than 200 mm undulations. And through its drive/control design method, the buffering of the landing gear landing process and the stability control of the fuselage are effectively realized. Compared with the traditional landing gear, this kind of landing gear has the advantages of fordable deployment, landing attitude adjustment, and complex terrain adaptation.

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.

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.

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.

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.

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.

National major missions such as flight-frequency transportation and manned lunar landing have raised new requirements for the development of advanced equipment, demanding even orders-of-magnitude leaps in the performance metrics of key components. However, traditional design and manufacturing methods are limited by the separation of material, structure, manufacturing, and function elements, making it difficult to meet these demands. There is an urgent need to develop new manufacturing technologies capable of achieving ultra-high-performance/function structures. Building on preliminary exploration and practice, the concept of meta-structure manufacturing and attempts to elucidate its essence and characteristics from the perspectives of dimension, scale, and order is proposed. It outlines the technical framework of meta-structure manufacturing, presents several research case studies, and finally analyzes and prospects its application scenarios from the viewpoints of material utilization, energy conversion, and information regulation. Through this paper, we aim to consolidate research efforts across the industry, break through traditional concepts and paradigms, and pioneer a new disciplinary field in meta-structure manufacturing technology.

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.

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.

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.

The PV-diesel-battery hybrid power system can extend the range of unmanned surface vessel for maritime patrol, keep missions uninterrupted, and improve the level of maritime rights protection in remote sea area. However, fluctuations in load demand power will occur due to the time-varying characteristics of wave height, and the photovoltaic power will change significantly due to the intermittency of solar irradiation density under complex sea conditions, which brings challenges to energy management. A real-time energy management strategy based on wave height and solar irradiation density prediction is proposed. A hybrid CNN-LSTM model combined with convolutional neural networks(CNN) and long short-term memory(LSTM) is utilized to predict wave height and solar irradiation density for acquiring load demand power and photovoltaic power. Model predict control (MPC) strategy is subsequently employed to optimize energy flow. The comparison is carried out between the proposed method and MPC with traditional prediction method. The results of hardware-in-the-loop experiment show that the proposed method significantly improves energy efficiency under high sea states. The dimensionless sea condition factor, composed of the maximum photovoltaic power and the average load demand power, is proposed to quantify the characteristic of complex sea conditions. The correlation analysis shows that the dimensionless sea condition factor is significantly correlated with the energy efficiency improvement. The proposed method has the potential to enhance the complex environment adaptability of the energy management strategy employed by the PV-diesel-battery vessels, thereby providing valuable engineering guidance.

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.

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.

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.

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.

Centrifugal compressors, steam turbines, and flue gas turbines, among other large rotating machinery, are core power equipment in the petrochemical enterprises. Intelligent diagnosis of common typical equipment faults is crucial for carrying out intelligent operation and maintenance. To address challenges such as the difficulty in extracting early-stage weak faults, poor noise interference resistance, confusion of signal features across different fault states, and low learning performance of common features in cross-condition data, the refined composite zoom multi-scale weighted permutation entropy index has been constructed, which is effective for capturing subtle oscillation patterns across the full frequency band. A method for cross-domain fault transfer diagnosis in rotating machinery is also proposed. Firstly, the method is initiated with the decomposition, filtering, and reconstruction of raw vibration signals from a multi-source typical fault database to extract their sensitive common features. Subsequently, the multi-kernel twin ensemble feature learning strategy is employed to iteratively enhance the model's feature classification performance for five types of source domain faults: rotor unbalance, shaft misalignment, static and dynamic rubbing, oil film whirl, and surge. Then, the semi-supervised manifold feature transfer strategy is used to minimize the differences in feature distributions between the target and source domains, with the strong classifier mapping and matching fault category labels. Finally, the effectiveness of the proposed method is validated using real engineering fault case data and compared against five published entropy features and six fault diagnosis methods from the references, demonstrating superior diagnostic performance of the proposed method under multiple operating conditions for different equipment.

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.

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.

Electro-mechanical transmission(EMT) integrates electric drives with transmission systems, yet they face rapid performance degradation under specific conditions, which can be addressed with active torsional vibration suppression. Traditional damping strategies, often based on simplified models, poorly match real-world scenarios and result in sluggish dynamic responses. To improve this, a 27-DOF torsional vibration model of EMT is developed, and a reduction method using modal contribution and stiffness sensitivity analysis is applied, effectively reducing the model to 5 DOFs while preserving 99.89% similarity in the 0-100 Hz range. Addressing the trade-off between strong vibration suppression and low response delay, the mechanism by which the driving torque excites torsional vibrations in the EMT is investigated. The dominant frequency and energy distribution of the driving torque in the frequency domain are optimized. The ideal transfer characteristics from disturbance to half-shaft torque are designed. Finally, based on the reduced-order model, an internal model control-based torsional vibration suppression(IMC-TVS) strategy for EMT was proposed. Simulation and experimental validations show that the IMC-TVS strategy has better advanced performance and real-time capability in torsional vibration suppression compared to PID and linear quadratic regulator(LQR) controllers.

High-strength rare-earth magnesium alloys have broad application prospects in aerospace, transportation and other fields. However, conventional methods obtaining high-strength magnesium alloys by increasing rare-earth contents raise material production costs. Therefore, there is an urgent need to develop a high-strength, low-cost magnesium alloy with low rare-earth content. The evolution of microstructure and properties of the Mg-5.3Gd-4.1Y-0.13Zr solid-solution magnesium alloy during rolling deformation and aging processes is investigated. The contribution of each strengthening mechanism is calculated, with a focus on the unique precipitation strengthening mechanism at peak aging. The results indicate that the cubic compound Mg₅(GdY), which exists throughout the entire aging period, is primarily distributed along the grain boundaries. Its size ranges from 40 nm to 300 nm, and it is relatively soft with poor strengthening effect. During peak aging, 2～6 nm ellipsoidal precipitates are observed to be finely dispersed within the matrix. Due to the relatively low rare-earth content, these precipitates only nucleate and do not grow, contributing significantly to the strengthening of the alloy. At peak aging, the alloy’s yield strength reached 341.14 MPa, with a contribution from precipitation strengthening of 159 MPa, accounting for 47% of the total. The ultimate tensile strength reached 504.40 MPa, which is superior to most Mg-Gd and Mg-Gd-Y alloy systems. This is a novel method for obtaining high-strength magnesium alloys under low rare-earth content by controlling the nucleation and growth behavior of the precipitates, thereby regulating their morphology and distribution. This approach provides new principles and ideas for designing and developing high-strength rare-earth magnesium alloys through precipitation control.

Research on lithium-ion battery lifetime prediction is a hot topic, but the complex degradation process involving multiple cells complicates the task. Current studies often have limited sample sizes and rely heavily on future current and voltage data, making true prediction impractical for real-world applications. Additionally, the extensive use of machine learning methods has caused a significant strain on computational resources. Therefore, significantly reducing the computational cost of prediction methods while ensuring prediction accuracy is of great practical significance. To address this, we first designed experiments to study the degradation process between individual batteries and battery packs. Then, we proposed a Lebesgue sampling-based migration-driven lifetime prediction method for lithium batteries, transitioning from individual cells to groups. This method introduces Lebesgue sampling techniques to extract features and construct differential models, optimizing neighborhood differences between the target domain and the source domain, and efficiently computing the probability distribution of the arrival time at the Lebesgue limit. The method trains on degradation data from individual batteries, successfully predicting the failure life of the battery pack. The proposed method achieved a prediction error of around 1.5% in both early and real-time prediction scenarios, while also achieving computational efficiency in the millisecond to second range, significantly surpassing many existing mature lifetime prediction methods in both accuracy and efficiency.

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.

Obtaining three-dimensional high-resolution flow field in mechanical structures is especially critical for mechanical design and optimization. However, due to the constraints on the size of mechanical structures and sensors, it is difficult to obtain the 3D high-resolution flow field data directly, and the deep learning method based on 2D convolution cannot handle the 3D time-varying flow field data directly. To address the above problems, this paper proposes a deep learning reconstruction method for 3D high-resolution flow field based on time-varying flow samples at finite measurement points. The high Reynolds number (Re=2.2×10⁴) flow around square cylinder were modeled and trained. A low-dimensional representation model of the 3D turbulent field is obtained, and then a high-precision coordinate-representation coding mapping is constructed to achieve a 3D high-resolution reconstruction model of the flow data, which is in good agreement with the target value. It is demonstrated that the proposed method can realize the high-resolution reconstruction of the 3D turbulent field, which can provide a new method for the analysis of time-history data based on the measurement sensors in mechanical flow problems.

In order to deal with the incidental wheel slipping behavior of high-speed trains under poor friction conditions, an optimal adhesion control strategy base on fuzzy identification of rail surface status was proposed. To achieve the purpose of optimal adhesion utilization, this method determines the similarities between the current rail surface and the standard rail surfaces through fuzzy logical reasoning, and then calculates the optimal creepage threshold which is subsequently referred by PID controller to adjust the motor torque. The analysis of the effects of different anti-slip control strategies on the wheel/rail dynamic interactions under complex adhesion conditions is involved in this study thanks to the high-speed train-track coupled dynamics model, which is established based on the vehicle-track coupled dynamics theory. The simulation results show that under complex adhesion conditions, the fuzzy identification approach is more robust than the variance identification approach in determining the rail surface status and calculating the optimal creepage threshold. The PID control is able to keep the motor torque at the proper value and the phenomenon of wheel slipping is restrained. Consequently, the optimal adhesion control based on fuzzy identification of rail surface conditions can effectively improve the utilization rate of wheel/rail adhesion.

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.