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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 optimize the electromagnetic characteristics and viscous friction loss of wet permanent magnet brushless DC motor(WPMBLDC) at high speed, an 8-pole 12-slot WPMBLDC was taken as the research object, and the finite element method was used to study the electromagnetic performance and internal flow field characteristics of the motor. In order to reduce the peak value of no-load cogging torque, output torque ripple and viscous friction loss, and improve the average output torque, the no-load back electromotive force of the motor should not exceed 250 V. The evolutionary algorithm is used to analyze the sensitivity and influence of stator slot structure parameters, armature diameter, air gap width, pole arc coefficient and permanent magnet thickness on the electromagnetic characteristics and viscous friction loss of the wet motor, and the optimization and experimental verification are carried out. The results show that the air gap width, slot opening and pole arc coefficient are the significant factors affecting the electromagnetic characteristics of the wet motor. The groove opening and the outer diameter of the rotor are the significant factors affecting the viscous friction loss of the wet motor. After optimization, the peak value of cogging torque is reduced by 48.23%, the average torque is increased by 53.5%, the torque fluctuation is reduced by 16.11%, and the viscous friction loss is reduced by 14.8%. The optimization effect is more significant.

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.

High-speed reducers are essential components in new energy vehicles, with their power loss and transmission efficiency directly impacting vehicle performance and reliability. A transmission efficiency model for the splash-lubricated high-speed reducer is proposed, considering gear meshing, oil splashing drag, and bearing power consumption. Gear meshing power losses are calculated using dynamics theory with friction models under elastohydrodynamic lubrication. Churning power losses are evaluated via a gas-liquid two-phase flow model. An experimental setup is designed and conducted for efficiency testing of reducers with input speeds up to 16 000 r/min, thereby validating the proposed model. Results indicate that oil splashing drag and lubrication deterioration significantly reduce efficiency at high speeds, with optimal efficiency shifting to higher speeds as torque increases. Additionally, tooth profile and lubrication conditions have substantial effects on transmission efficiency. Optimizing pressure angles and tooth height coefficients, along with designing appropriate oil immersion depths and thermal equilibrium temperatures, can effectively reduce system power loss and enhance transmission efficiency.

During the mission of the launch vehicle, the separation mechanism, as a key component, is responsible for effectively detaching various stages of the rocket structure, with its reliability directly influencing the mission success rate. In recent years, as reusability has become a fundamental requirement in rocket design, non-pyrotechnic separation mechanisms have attracted increasing attention for their high reliability and safety, creating an urgent need for comprehensive reliability analysis to fully leverage their advantages in repeated applications. The definition, types, and working principles of separation mechanisms are detailed, and a systematic review of existing methods and the significance of mechanism and structural reliability research is presented. Existing failure mode identification approaches and reliability analysis methods are examined, highlighting their limitations when applied to reusable non-pyrotechnic separation mechanisms. The future development trends of reliability research on reusable non-pyrotechnic separation mechanisms are also discussed, aiming to provide a reference for theoretical research and technological innovation in the system reliability analysis of these mechanisms.

To address key challenges in aviation intelligent manufacturing, such as low data-knowledge collaboration efficiency, difficulties in tracing assembly deviation sources, lagging process parameter optimization, and insufficient virtual-real interactive verification, this study proposes an AI twin control methodology framework with industrial large models as the cognitive engine, and constructs a digital twin closed-loop control framework covering the entire "perception-diagnosis-decision-verification" process. Through networked associative modeling of knowledge graphs, semantic fusion and dynamic reasoning of multi-source heterogeneous data are realized, an industrial large model corpus for aviation manufacturing is established, and a professional knowledge base with autonomous evolution capabilities is formed. An industrial large model algorithm library for multi-scenario intelligent decision closed-loops is developed: Bayesian causal inference is used to analyze multi-level coupled causes of assembly deviations; incremental ensemble learning is integrated to achieve dynamic evolution prediction of multi-source coupled deviations; and transfer reinforcement learning is applied to break through the bottleneck of cross-scenario parameter optimization. Finally, a virtual-real bidirectional driven verification closed-loop is built using digital twin technology. Verification results based on the fuselage panel assembly of a certain type of civil aircraft show that the proposed method significantly improves the automatic assembly accuracy of stringers, with the one-time assembly and adjustment success rate of stringers increased by 24% compared with traditional methods. It also enables real-time inspection of drilling and riveting quality, achieving an accuracy rate of 98% in identifying continuous drilling and riveting defects. By constructing and evolving a domain-specific knowledge base, this study deeply drives the full-process closed-loop from deviation causal tracing to twin verification, and realizes a paradigm shift in manufacturing decision-making from experience-driven to model cognition-driven.

Intelligent machining, as the core direction of manufacturing industry's transformation to high precision and autonomy, is highly dependent on data enablement technology for in-depth analysis of heterogeneous data from multiple sources and intelligent decision support. In this study, we systematically sort out the research progress of data enablement technology in the field of intelligent achining, put forward a full-process enabling framework covering “On-machine measurement-Signal pre-processingFeature extraction-Multi-source fusion-Data governance”, and analyze three key technologies, namely, multi-source sensing information fusion cloud-fog-edge collaborative computation, and dynamic migration of process knowledge. The study shows that the data enabling technology is effective and efficient. The study shows that data enablement technology effectively enhances the real-time response and robustness of the process, significantly strengthens the guarantee of process control and quality consistency, and promotes the leap of intelligence level in multiple industrial scenarios. However, bottlenecks such as the difficulty of modeling multi-physical field coupling in complex process scenarios, the lack of fidelity of virtual-reality mapping, and the low efficiency of knowledge migration from small samples still restrict the application of the technology. Aiming at the above challenges, this paper proposes a future technology path that integrates dynamic federated learning and causal reasoning to provide a systematic research perspective for the manufacturing industry to evolve to a higher-order stage of cognitization.

Contact electrification (CE) is a common yet enigmatic physical phenomenon, characterized by its universality, high sensitivity, and ability to generate strong electric fields and electrostatic forces. The features of the CE research system are delineated, including the electron and ion transfer model at metal-metal and insulator friction interfaces, as well as the tribovoltaic effect at semiconductor interfaces. Various methods for controlling charge transfer at triboelectric interfaces are summarized. Furthermore, several applications of CE such as triboelectric nanogenerators and intelligent sensing technology are discussed. Lastly, it is emphasized that future research on CE will focus on charge control methods as the core and surface/interface design as the foundation, highlighting trends in mechanistic quantification research, multidimensional/extreme regulation methods, and multifunctional device integration.

The in-situ indentation testing inside the scanning electron microscope(SEM) is an effective technology for investigating and revealing the deformation damage process and mechanism of materials under contact loading. However, this technology is basically monopolized by foreign countries. Accordingly, an in-situ micro/nano indentation testing device inside the SEM with independent intellectual property rights is designed and developed, and its size is 130 mm×56 mm×53 mm. The structure, electromechanical system and testing processes of this device are described in detail, along with the calibration and performance testing of its core components. Corresponding to the load range of 500 mN and displacement range of 19 μm, the load noise and displacement noise are less than 0.03 mN and 2 nm respectively, which meets the performance requirements for micro/nano indentation testing. Using the reference mapping method, the frame compliance of the developed device is calibrated. The load-depth curve obtained by the device after calibration basically coincides with that obtained by the commercial indentation instrument. Finally, the in-situ indentation tests of the Zr-based metallic glass are performed inside the SEM by using the cubic corner indenter. The effects of maximum indentation load and loading rate on the serrated flow behavior and the characteristics of indentation-induced shear bands of metallic glasses are investigated, verifying the practical application potential of this device for performing micro/nano indentation testing inside the SEM.

Intelligent fault diagnosis method based on data-driven is the research focus of modern mechanical system. However，due to practical limitations，it is impossible to obtain samples of all working conditions or fault types，which makes the data-driven model lack of specific training data，leading to unsatisfactory performance of the method. In view of the above challenges，a rolling bearing fault diagnosis model based on contrastive embedding feature generation is proposed. By learning the faults with sufficient samples, the common features of existing faults and missing faults are mined to realize the generation and diagnosis of specific types of missing faults. Firstly, the spectrogram of the corresponding fault characteristics is obtained by analyzing the time-frequency characteristics of the original vibration signal; Secondly, the feature extraction network which has been pre-trained and fine tuned is used to extract the fault features in the spectrogram; Then, the extracted features are input into the confrontation network based oncontrastive embedding feature generation and Wasserstein generative adversarial network with gradient penalty. Through the cross stage contrastive embedding method, and according to the pre-defined fine-grained fault description, the feature generation of missing faults is completed，and the actual and generated fault features are mapped into the embedding space; Finally, the classification of all fault types including known and unknown is completed in the embedded space. The proposed method is applied to three typical zero-shot fault diagnosis scenarios. The experimental results show that compared with other methods, the proposed method can diagnose the known and unknown fault types, has advantages in accuracy.

With the continuous expansion of new energy power generation, the demand for energy storage is significantly increasing. Electrochemical energy storage technology, represented by lithium-ion batteries, has become the dominant technology in this field due to its high energy density and long cycle life. However, safety issues in large-scale lithium-ion battery energy storage power stations have become increasingly prominent in recent years, with frequent thermal runaway and fire accidents. The internal temperature of a lithium-ion battery is regarded as a key indicator of its operational status, providing precise early warning for thermal runaway. Currently, the Battery Management System(BMS) primarily monitors temperature using sensors placed on the battery surface, but this method is associated with measurement errors and response delays. First, the microstructural changes and thermal runaway mechanisms during internal temperature rise in lithium-ion batteries are introduced. Then, an analysis and review are conducted from three aspects: internal temperature measurement using embedded sensors, internal temperature estimation based on Electrochemical Impedance Spectroscopy(EIS), and internal temperature prediction using machine learning algorithms. By comparing the advantages and disadvantages of these three methods, research progress is systematically summarized, and future development directions are prospected. The research results provide a theoretical reference for the accurate prediction of lithium-ion battery temperature in BMS and offers new ideas and a foundation for research on thermal runaway early warning.

In Part I, the methods of kinematics modeling and system parameter identification of robot-tracking system are studied, simultaneous calibration model of dual-robot tracking/measure-machining integrated system is established, and a method of smoothing machining trajectory generation of skin parts is proposed. The robotic tracking/measure-machining system has a good initial geometric accuracy, and the executable robot milling trajectory is generated. However, when the machining robot moves in a wide range, it is affected by several geometric and non-geometric errors such as kinematic parameter error, joint angle error, weak stiffness deformation, joint friction error and cutting vibration error. Generally, the trajectory accuracy is only in the millimeter level, and the robotic machining system without feedback compensation is difficult to accurately process the skin contour. Therefore, Part II further establishes the closed-loop feedback control model of the robot’s end pose under the high-precision external tracking system, develops the corresponding hardware and software system, and achieves the real-time tracking and feedback compensation of the robot's end pose based on the ±10μm measuring accuracy originating from the external tracking system. Finally, the simultaneous calibration accuracy test of dual-robot, the trajectory accuracy test of end pose with closed-loop control, and the robotics machining accuracy test of skin samples are practically verified.

Embodied interaction based on multimodal technology allows designers to create in an "all-in-one" manner, and the impact of this interaction on creative thinking in the design process needs to be explored in depth. The neurophysiological measures of designers in traditional “keyboard + mouse” interaction and embodied interaction are compared to investigate the influence of embodied interaction on designers' creative thinking. Thirty-four designers complete a three-dimensional composition design task in a computer-aided design system using the traditional and embodied interaction respectively. Cognitive data such as electroencephalograms, heart rates, workload scales and verbal reports are collected simultaneously under both interaction modes. In addition, the design solutions are assessed for creativity using the Consensus Assessment Technique. The results of the data analysis show that, (i) the alpha band power of the designer’s parietal and occipital regions under embodied interaction is significantly lower than that of traditional interaction, indicating that the connection between the body and the virtual design space enhances the designer's visual processing and facilitates the generation of creative thinking; and (ii) the suppression of alpha and beta bands of the designer's sensorimotor region under embodied interaction suggests that the consistency of the designer’s body movements, postures, and daily experience accelerates designers' understanding of movement metaphors, which has a stimulating effect on creative thinking; (iii) the significant increase in the designer’s electrodermal activity and heart rate values under embodied interaction suggests that the novelty and flexibility of the operation process can bring about emotional arousal for designers, which is conducive to the generation of creative thinking; (iv) designers need more training and have a heavier workload in embodied interaction, which may have a negative impact on creative thinking. This suggests that the maturity of multimodal technology needs to be further improved in the future.

The friction pair of SiCp/A356 brake disc and synthetic brake pads often have problems such as friction surface scratches and brake noise during braking. The synthetic brake pads paired with SiCp/A356 brake discs are selected as the research object. Structural optimization design of the synthetic brake pad and friction characteristics of different synthetic brake pad materials are investigated using a combination of scale bench testing and simulation methods. Scale bench tests reveal that debris accumulation in radial grooves on both sides of the brake pads is more severe than in circumferential grooves, while accumulation in the cut end exceeds that in the non-cut end. Through flow field simulation analysis, airflow in the vortex airflow region within pad grooves is identified as insufficient, leading to debris accumulation tendencies. Response surface analysis based on orthogonal testing and Box-Behnken test design is employed to optimize pad structure design, aiming to enhance air mass flow rate without reducing convective heat transfer coefficient. The optimized synthetic pad increases the air mass flow rate by 28.22%, the convective heat transfer coefficient by 1.34%, the performance of chip removal and heat dissipation are improved, the coefficient of tendency of instability is decreased by 49.53%, and brake noise tendency is reduced. Friction matching between SiCp/A356 composites and three synthetic materials is examined using the optimized brake pads structure. FANY and 928W materials are found to maintain stable third body layers, demonstrate low wear rates, and exhibit stable average friction coefficients, with FANY material showing the lowest braking noise levels.

An artificial intelligence architecture—the chain-of-thought (CoT) paradigm text-based multimodal intelligent agent—for operation and maintenance (O&M) of mechanical equipment is proposed. Firstly, to address the challenge of constructing high-quality, large-scale monitoring data-to-fault mode mapping datasets in real-world engineering applications, a chain-of-thought dataset construction strategy integrating monitoring signals, mathematical features, text descriptions, and fault mode is proposed. Based on this, a signal-to-text (Sig2Txt) model driven by a signal-text data generator is developed. Subsequently, a high-quality specialized textual dataset for O&M in the mechanical equipment domain is created, and an intelligent O&M-specialized large language model is established through instruction fine-tuning on a general large language model. Finally, by organically integrating the above models based on large model intelligent agent technology and guided by the operational thinking patterns of human experts in equipment maintenance, a chain-of-thought paradigm text-based multimodal intelligent agent for intelligent O&M is formed. Testing results indicate that this model can achieve chain-of-thought parsing and mapping from multimodal input decision-making, with an accuracy exceeding 70% on ISO Category III vibration analyst test questions, thus reaching expert-level performance. In evaluations with engineering cases and publicly available multimodal datasets, the proposed model outperforms existing mainstream large models. More importantly, owing to the proposed low-cost, high-quality multimodal CoT large-scale dataset construction framework, and the unique advantages of a “text-based” approach in terms of encompassing knowledge, high-level abstraction, and interpretability, the model shows considerable scalability and development potential.

To address the challenge of balancing real-time performance and adaptability in hybrid electric vehicle (HEV) energy management, this paper proposes a real-time hierarchical energy management strategy (EMS) that integrates deep reinforcement learning (DRL) with model predictive control (MPC). At the upper layer, a deep Q-network (DQN) is employed to construct an EMS controller that rapidly plans a reference trajectory for the state of charge (SOC) prior to vehicle departure. At the lower level, a Long Short-Term Memory (LSTM) network is first employed to construct a velocity predictor, forecasting the velocity sequence over a future time domain. Subsequently, an MPC controller is designed to achieve optimal power flow allocation by tracking the SOC reference trajectory. The proposed strategy is then comprehensively compared with dynamic programming (DP) and rule-based strategies across different test conditions. Simulation results demonstrate that the proposed strategy achieves over 90% of the fuel economy attained by the DP strategy while exhibiting strong real-time application potential. Finally, hardware-in-the-loop (HIL) experiments validate the practical applicability of the proposed strategy.

Optimizing additive friction stir deposition manufacturing processes has been an urgent need. In order to overcome the problem of uneven distributed mechanical performance, a study of additive friction stir deposition manufacturing process for 6061 aluminum alloy has been conducted. The effects of traveling speed and shoulder height on the macroscopic morphology, microstructure, and mechanical properties of the deposited aluminum alloy are analyzed. A physical model of material filling during the deposition process is also established. The results show that internal weak bonding in deposition layer is the main cause of the decrease in tensile performance along the deposition direction. When weak bonding occurred, the arc-shaped pattern on the surface of the deposition layer became shallower or disappeared in local areas. The formation of weak bonding can be suppressed by setting the actual feeding rate greater than the theoretical feeding rate required to just fill the preset gap in a single rotation cycle of the shoulder, which improve the metallurgical bonding quality and mechanical properties. The proposed calculation methods for material fill volume and feed rate based on the deposition process of plasticized materials provide a theoretical basis for a deeper understanding of the friction stir additive process and for improving deposition quality.

As a multi-functional composite structure that integrates wave transmission, load bearing and temperature resistance, wave-transmitting material and structure play an important role in radar radome, stealth skin and 5G communication components. A method for the preparation of glass fiber reinforced thermoplastic polyether ether ketone (PEEK) polymer wave-permeable 3D printing filament is proposed. The mechanical and wave-permeable properties of the PEEK-based filament are analyzed, and the effects of varying glass fiber content on these properties are investigated. Optimal material processing parameters are determined to achieve high-efficiency wave-permeable performance. Leveraging the advantages of fused deposition modeling 3D printing, a honeycomb sandwich wave-transmitting metamaterial structure is designed and fabricated for composite materials. The electromagnetic test results indicate that the wave transmittance of the designed metamaterial achieves 94.36% and 94.62% in the frequency range of 2-18 GHz in TE and TM mode, respectively. The method of high efficiency transparent printing wire controllable manufacturing and transparent metamaterial controllable design and manufacturing provide effective support for the realization of stealth radome and stealth skin.

Advanced materials are new materials with excellent properties, which are characterized by high strength, extreme temperature resistance, high biocompatibility, or special functions such as self-healing, shape memory and environmental response. Advanced materials have become the key foundation to support the development of high-end manufacturing. However, due to the high hardness, high melting point, high brittleness and other characteristics of advanced materials, the disadvantages of traditional processing methods are gradually revealed. The additive manufacturing technology based on material layer by layer cumulative forming is especially suitable for advanced materials to form complex three-dimensional structures, and has made significant progress in product development and industrial application. Therefore, a review of the research and development in additive manufacturing of advanced materials is provided. Firstly, the related concepts of advanced materials and additive manufacturing are elaborated, and the development prospect of their synergy is analyzed; Secondly, based on different forming principles, the development status and technical advantages of various advanced material additive manufacturing are introduced; Thirdly, the advanced material system is systematically reviewed, and its application status in additive manufacturing is summarized; Then the application status of the technology in different fields is shown, and the actual cases are listed; Finally, the existing problems of this technology are discussed, and the future development direction is prospected.

To overcome the limitations of a single non-destructive testing method, a sensing structure that integrates magnetic flux leakage（MFL） detection and electromagnetic acoustic transducer（EMAT） detection methods is proposed to simultaneously detect surface and internal defects in steel specimens. The proposed hybrid sensor structure makes full use of the magnetic circuit characteristics of the MFL and EMAT. The combination of the yoke and permanent magnets creates a horizontal magnetic field inside the sample to detect defects on and near the surface of the sample. Secondly, the local vertical magnetic field provided by the magnetization structure of the MFL interacts with the EMAT excitation coil to produce ultrasonic waves in the specimen to identify internal flaws in the specimen. The excitation source for MFL is quasi-static and the ultrasonic signal is a pulsed excitation source at 2 MHz. The significant frequency difference between the two signals is helpful in reducing mutual interference. Simulations and experiments show that the proposed sensing structure can overcome the blind zone caused by near-field emission in EMAT and the depth limitation of MFL, and is able to simultaneously detect and classify both surface defects and internal blind hole defects in large thickness ferromagnetic sample.

Wide hot-rolled strip is a core raw material for modern manufacturing，and its efficient large-scale production combined with refined quality management is essential for industrial upgrading. However，the hot rolling process is characterized by strongly coupled multivariable factors，along with limited material information sensing，fragmented data systems，and insufficient automation in critical stages. These challenges hinder the coordinated optimization of product quality，rolling stability，and manufacturing cost. This research systematically reviews recent technological advances，with a focus on progress in the following key areas. In the field of multidimensional material information sensing for hot continuous rolling，robust sensing systems based on machine learning and deep learning have addressed the difficulty of feature extraction under high-temperature and high-noise conditions，substantially improving information acquisition in complex conditions. In the area of multi-zone operational interconnection and edge–end collaboration，the integration of heterogeneous data from multiple sources and the use of dynamic resource allocation mechanisms have eliminated information discontinuities among storage yards，rolling lines，and roll grinding systems，enabling coordinated optimization across regions. In centralized control of multiple rolling lines and regions in hot continuous rolling，intelligent rolling technologies oriented toward reduced manual intervention have been developed by leveraging advanced inspection systems and high-accuracy hybrid modeling，thereby enhancing the compactness and responsiveness of the production process. In cross-business coordination for hot continuous rolling，lean management platforms integrating online prediction，real-time monitoring，and anomaly diagnosis have been established within a deeply integrated cyber–physical framework，driving transformation in production management across multiple business domains. Finally，the paper summarizes current developments and outlines future directions for efficient large-scale production and refined quality management of wide hot-rolled strip.

Aiming at the lateral sway of the tail car when a domestic type of urban EMU with a speed of 160 km/h is running in a single-track tunnel, the influence mechanism of aerodynamic excitation on the lateral sway of tail car was revealed, and the mitigation measures are studied from the perspective of suspension parameters optimization. According to the actual shape and dynamic parameters of the city EMUs, the aerodynamic and multi-body dynamic models of the 8-car train are established respectively. Overset mesh technique is used to obtain the aerodynamic forces of train, which are applied to the vehicle body as external excitations, and then the fluid-solid coupling simulation model of the train is constructed. And the root locus analysis method is used to grasp the evolution of vehicle suspension modals and hunting modals, the results show that in the single-track tunnel, the aerodynamic forces on each car from front to rear gradually increase, and the difference between the head car and the tail car is very obvious. The aerodynamic forces of the tail car are in the form of harmonics, and the main frequency of each aerodynamic force is in the range of 0.5-3 Hz, which is close to the vibration modals of the vehicle system itself. The upper rolling modal and yawing modal of the vehicle have modal-hopping and coupled resonance with in-phase and out-phase of hunting motion of two bogies respectively, which causes primary serpentine of train. The main frequency of the large yaw aerodynamic moment have the coupling resonance with the hunting modal frequency of the whole vehicle，which is the important reason for the lateral sway of the tail car. Changing the hunting frequency or increasing the damping ratio by optimizing the suspension parameters of vehicle is an effective measure to alleviate the sway of the tail car. Reducing the damping coefficient of the anti-yaw damper, increasing the damping coefficient of the secondary lateral damper, reducing the longitudinal stiffness of the primary, increasing the equivalent conicity, and adding the lateral damper between front and rear cars can relieve the lateral sway of the tail car to a certain extent, and improve the running stability and ride comfort.

Agricultural equipment is widely used in China, which has led to increasingly resource and energy consumption problem as well as environmental emissions. Promoting development of green agricultural equipment is an issue that needs to be addressed. Green design of agricultural equipment is an effective method to solve the above-mentioned problem from the perspective of manufacturing, packaging, transportation, operation and maintenance, scrapping and recycling process. Hence, the concept of green design for agricultural equipment is first proposed. Then the connotation and principles in green design of agricultural equipment are proposed. Secondly, the green design procedure of agricultural equipment is constructed. On the basis, the similarities and differences between green design and conventional design of agricultural equipment from the aspects of objectives, boundaries, basic data, tools, product attributes are analyzed. Finally, the framework for green design of agricultural equipment is presented, which includes green material selection, lightweight design, modular design, energy-efficient design, easy disassembly design and remanufacturing design. Moreover, the points and applications of using these technologies are introduced. The framework can be a useful tool for green design of agricultural equipment.

As the actual surfaces of manufactured parts are inevitably different from the designed ideal ones, how to quickly and accurately establish a model that can describe such differences as geometric defects, has always been challenging in tolerance analysis. Current geometric defect models usually simplify geometric errors into rigid translation and rotation of ideal surfaces, ignoring the influence of surface topography on the relative position among parts, which in turn restricts the accuracy of tolerance analysis results. As a model of geometric defects based on the discrete geometry framework, skin model shapes (SMS) enable to reflect the geometric deviations more realistically in tolerance analysis. Therefore, the emergence of SMS has attracted widespread attention from scholars. However, the current research on SMS is characterized by a lack of systematic organization, and there is no consensus regarding its relative importance. This study systematically reviews the origin of SMS. The characteristics and connotations of SMS are further clarified. A technical framework of SMS technology is proposed, extracting three technical issues of SMS, including the definition of non-ideal surfaces, the law of geometric error distribution and the mechanism of multi-physical fields. Three types of methods, in the context of feature operations, geometric error modelling and multi-physical field characterization, are summarized as key enablers in the framework. Then, existing modelling methods for geometric defects and their typical applications are analysed in detail. Three core challenges currently faced by SMS at the theoretical level, technical level and application level are pointed out. Finally, three directions for the future development of SMS technology are highlighted.