The technology of humanoid robots is currently evolving rapidly, becoming a new focal point for global technological innovation and industrial upgrading. As an important representative of embodied intelligence, humanoid robots possess vast development potential and application prospects. Based on the multidisciplinary intersections, complex systems, and high levels of integration inherent in humanoid robot technology, this review synthesizes the latest research achievements and industry developments in this field, focusing on the current technological status and development trends of humanoid robots. First, the definition and developmental history of humanoid robots are introduced, describing the current status of development in both foreign and domestic contexts from the perspectives of technological level, industrial landscape, and policy support. A comparison and summary of the typical technological development characteristics and product features between domestic and international advancements are provided. Key core technologies are analyzed in detail, including critical components, environmental perception and scene understanding, gait control and dexterous manipulation, embodied intelligence and large models, human-robot collaboration and interaction, as well as operating systems and toolchains. The implementation pathways and current research progress of these technologies are discussed. Furthermore, typical applications of humanoid robots in specialized service environments, intelligent manufacturing, and household and social services are presented, exploring their expansion potential in emerging application areas. The main challenges faced by humanoid robot development are analyzed, focusing on technological bottlenecks and application difficulties. Finally, based on the development status of technologies and applications, an outlook on the trends in embodied intelligence represented by humanoid robots is provided, particularly in areas such as multimodal vertical large models, high-performance simulation training platforms, and safety and ethics. This review aims to summarize and grasp the dynamics of cutting-edge technological developments in humanoid robots domestically and internationally, while offering insights and references for those engaged in the research and development of humanoid robot technologies and products, thus contributing to the advancement and industrialization of humanoid robot technology in China.

The European Union’s Industry 5.0 initiative introduces a new era of intelligent manufacturing that emphasizes a human-centric approach, driving the rapid advancement of human-centric manufacturing（human-centered smart manufacturing）. As one of the core paradigms of this concept, human-robot collaboration（HRC） has emerged as a key research focus in the industrial manufacturing domain in recent years. This study conducts a comprehensive analysis of the past and future of HRC, focusing on the following aspects: reviewing the development and evolution of human-machine relationships, exploring the iterative progression and integration of HRC models, summarizing the typical applications of HRC across various fields, and envisioning future development goals and technological breakthroughs. The evolution of human-machine relationships is elucidated by examining the coupling between industrial development trajectories and the increasing empathy between humans and machines. Based on the characteristics of human-machine relationships and collaboration, the iterative advancements and integration of HRC models in manufacturing systems are analyzed and summarized. Three typical modes of HRC—human-machine interaction, human-machine coordination, and human-machine symbiosis—are reviewed for their applications in fields such as product assembly, robotic control, and autonomous driving. The shortcomings and challenges of these modes in practical applications are also discussed. Finally, the future vision and developmental directions of HRC are outlined, with an emphasis on the new technologies and theories needed to overcome existing challenges in the era of advanced human-robot collaboration. These efforts aim to propel the manufacturing system toward a new level of human-centric intelligent manufacturing.

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 continuous deep integration of new generation information technology and manufacturing technology, the human-centric smart manufacturing paradigm is reshaping traditional industrial production models. Human activity recognition technology, as a key enabling technology for implementing human-oriented smart manufacturing, primarily focuses on intelligent recognition and understanding of human activity semantics, which shows broad application prospects. A systematic exploration of the current development status, key challenges, and application prospects of human activity recognition technology in industrial scenarios helps promote theoretical development and innovative practices of human-oriented smart manufacturing. First, based on the developmental trajectory of human activity recognition technology, this study deeply analyzes the evolution process of core technologies such as human perception, activity modeling, and activity recognition, laying the technical foundation for industrial applications of human activity recognition technology; second, focusing on the special requirements of industrial scenarios, it emphasizes research on key technologies including robust multi-modal perception systems, multi-scale activity understanding frameworks, human-machine collaboration with integrated intention understanding, and optimized deployment in industrial scenarios; on this basis, it systematically analyzes and evaluates the quality of human activity datasets in industrial scenarios, and highlights the practical progress of human activity recognition technology in typical application scenarios such as production safety control, production scheduling optimization, process improvement, and activity enhancement; finally, combined with emerging technologies such as spatial intelligence, physiological-cognitive integration, and multi-modal large language models, it envisions future development directions for human activity recognition technology in industrial settings.

Non-invasive brain-computer interface（BCI） technology, as an emerging human-computer interaction method, has demonstrated broad application prospects in the field of robot control. This study firstly outlines the background and importance of its development, and deeply discusses the physiological basis of brain electrical activity, clarifying how electroencephalography（EEG） has become a common measurement tool for BCI systems due to its non-invasiveness and convenience. Subsequently, this study analyzes the advantages and disadvantages of typical EEG paradigms and applicable scenarios-including active ones such as motor imagery, reactive ones such as steady-state visual evoked potential（SSVEP）, event-related potential P300, and hybrid paradigms that combine the advantages of multiple paradigms. hybrid paradigms that combine the advantages of multiple paradigms, showing how these paradigms can realize complex and efficient robot control tasks. In addition, this study systematically introduces the key steps from EEG signal acquisition to preprocessing and pattern recognition, emphasizes the role of deep learning in improving decoding accuracy, and also points out its challenges, such as high data volume requirements and poor model interpretability. Finally, this study summarizes the development trends and research challenges of BCI technology, and proposes directions to promote the further development of non-invasive BCI technology in practical robot control applications. In summary, this study not only provides an exploration of the application of non-invasive BCI technology in robot control, but also emphasizes the transformative impact that this technology may bring in the future, providing reference and inspiration for subsequent research.

The surface topography and roughness of workpieces are critical metrics in grinding processes, with accurate prediction considered essential for advancing intelligent manufacturing. The generation of workpiece surfaces during grinding is recognized as a complex, stochastic process, and the accuracy of existing physics-based predictive models is deemed insufficient. A comprehensive review of predictive models and methodologies for workpiece surface topography is presented, with emphasis placed on geometric and kinematic aspects of grinding. Six geometric modeling approaches for abrasive grains, including the random plane method, are summarized, and the influence of abrasive grain parameters on model fidelity is examined. Mathematical models for the random distribution of abrasive grain positions and orientations on grinding wheel surfaces are reviewed, and the effects of model parameters on features such as protrusion height are analyzed. Methods for the fabrication and conditioning of grinding wheels with controlled abrasive grain arrangements are also discussed. Kinematic models of abrasive grains for various grinding processes, including plane and ultrasonic-assisted grinding, are analyzed. The interaction mechanisms between abrasive grains and the workpiece surface under different conditions are explored, and predictive models for surface roughness are generalized based on dynamic abrasive grain models. Finally, prediction errors of existing roughness models are statistically analyzed, with error ranges identified from 4.47% to 37.65%, and an average error of 11.59% determined. New perspectives for improving the prediction of grinding surface topography and roughness are proposed, offering references for the development of intelligent predictive methods integrating grinding mechanisms with data analysis.

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.

Accuracy retentivity is one of the key performance indexes of machine tools, which describes the ability of machine tools to maintain their original accuracy. The evaluation of accuracy retentivity is the theoretical basis for the accuracy retentivity improvement project of machine tools. Aiming at the existing evaluation methods of machine tool accuracy retentivity of the indication system is not sound, the lack of comprehensive evaluation methods and other issues, the meanings of inherent accuracy retentivity and service accuracy retentivity of machine tools are made clearer in this research. A static-dynamic indication system of accuracy retentivity including accuracy margin, accuracy degradation amount, accuracy degradation rate, accuracy retention degree and accuracy retention time is established. This system describes the machine tool's capacity to retain accuracy from a variety of angles, including degradation conditions, degradation processes, and degradation outcomes. A combined static/dynamic evaluation method for absolute comprehensive evaluation of accuracy retentivity is proposed. The evaluation steps include: discrete accuracy degradation data functionalization, dynamic comprehensive evaluation of the accuracy degradation process and static comprehensive evaluation of the accuracy retention capability. By combining cases, it confirms the efficacy of the given method and model.

The industrial metaverse is a vertical application of metaverse technology in the industrial sector, which reshapes the production and manufacturing mode and industrial ecology. With the rise of Industry 5.0, the concept of human-centric smart manufacturing has gradually gained attention. The development of the industrial metaverse focusing on human-centric smart manufacturing has realized the deep integration of the two emerging concepts and has shown great research value and potential. Therefore, a digital twin modeling and distributed virtual collaboration method of human-centric smart manufacturing system in the context of industrial metauniverse is proposed. The aim is to address the challenges of rapid customization of human digital twin models and efficient collaboration among multi-human in a virtual space. A metamodel-based method for the rapid customization of human digital twins is developed, and virtual-real mapping and dynamic interaction between humans and their digital twins are realized with machine vision, which lays the foundation for the industrial metaverse. Next, a multi-human collaboration method for the metaverse space is proposed and a server-client distributed access framework is established to realize the integration and interaction of multi-human digital twins, which provides support for multi-person remote, real-time online, and effective collaboration. Finally, the effectiveness of the proposed approach is validated through a dual-human collaborative loading and unloading scenario in a human-centric smart manufacturing system.

New principles and methods of multi-field assisted micro-machining have emerged to achieve performance-geometry-integrated manufacturing for micro-structures or functional surfaces. However, the synergistic mechanism between the creation of surface integrity under energy fields coupling is still well understood, which makes it difficult to provide precise guidance for its industrial application. This study focuses on the field-assisted micro-grinding composite processing technology. Based on the analysis of the existing technical bottleneck of micro-structure machining under size effect, the synergistic mechanism of electrochemical, laser, ultrasonic and other energy fields in improving material machinability and improving processing efficiency and quality is discussed. Taking typical field-assisted micro-griding technologies as examples, whose process principle, application characteristics and existing challenges are reviewed. It also presents future work in the areas of field-assisted technology innovation and equipment development. The aim is to provide theoretical guidance and technical support to the academic and industrial communities.

Aerospace vehicles place higher demands on the manufacturing process and service for traditional high-strength aluminum alloy thin-walled components in terms of new concept, long life and reliability. The implementation of high-performance forming methods currently shows an urgent problem that needs to be solved for such complex components. First, the huge challenges were analyzed for the overall forming of high-strength aluminum alloy thin-walled components. Based on the discovery of the dual enhancement effect of aluminum alloys at cryogenic temperatures, the proposal background is summarized for the cryogenic forming technology. Then, comprehensive analyses on the dual enhancement effect and micro deformation mechanism were conducted for aluminum alloys at cryogenic temperatures by domestic and foreign scholars in recent years. Also, the in-situ testing method for macro and micro cryogenic deformations, cryogenic forming process and key technology, cryogenic forming equipment and typical applications were studied. Finally, the future development was discussed for the cryogenic forming of aluminum alloys. These researches can provide a new approach for the manufacture of aluminum alloy complex integral-curved components relating to aerospace vehicles, electric vehicles and new energy storage and transportation equipment.

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.

Gears are the core components of electric drive transmission systems in new energy vehicles, exerting a significant impact on vehicle performance. With the rapid increase in new energy vehicle penetration rates and the continuous enhancement of power density in electric drive transmission systems, gears now face high-service-performance challenges including high-speed operation, low noise, and fatigue resistance. Achieving high-efficiency precision machining represents the fundamental approach to ensuring their superior service performance. However, there are still some difficulties in the efficient precision machining of new energy vehicle gears, including the generation mechanisms, key technologies, and machining equipment. This paper systematically reviews current research progress regarding the high-performance tooth surface generation mechanisms, key technologies for efficient precision machining in typical processes such as worm wheel grinding and internal meshing power honing, as well as advanced gear machining equipment. It further summarizes and prospects the development trends of high-efficiency precision machining technologies for gears, providing theoretical and technical guidance for subsequent research.

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 lifecycle value chain collaboration enables to improve the collaborative efficiency and response speed of the entire process of complex product design, manufacturing and operation, and to promote the collaborative control services and value co-creation of value chain enterprise groups. This article firstly analyzes the research development of value chain collaboration for complex products and summarizes the conceptual connotations and multidimensional evolution characteristics. Subsequently, a theoretical framework for the lifecycle value chain collaboration for complex products is proposed, with value activities as the main thread, clarifying its organizational structure, value-added mechanism, and dynamic control logic. Based on the proposed framework, the research directions of value chain collaborative business modeling and process integration, multi-dimensional element interconnection and decision-making as well as collaborative operation process control and optimization are discussed, and their current research status and shortcomings are analyzed. Finally, the application cases of the lifecycle value chain collaboration in typical manufacturing industries are explored and studied. By analyzing the pattern characteristics and operation mechanisms of different industry applications, the future developing trends of the lifecycle value chain collaboration, i.e., integration, lean and closed-loop, are pointed out.

In the transition from Industry 4.0 to Industry 5.0, human-centric smart manufacturing（HSM） represents an innovative paradigm in the development of smart manufacturing systems. In the context of HSM, human well-being is recognized as its core value which aims to redefine and reinforce the central role of humans in manufacturing and production processes. Therefore, HSM is promoting the futuristic industry which is human-centric, sustainable, and resilient. Human motion is the key to realizing human movement intentions as well as to promoting the development of HSMs. This work focuses on human motion digital twin（HMDT）, reviews its enabling technologies and research advancements, specifically focusing on human motion modeling, perception, and analysis, with an emphasis on their pivotal applications in three dimensions, i.e., unit level, production line level, and workshop level. Through case study, this work illustrates how HMDT facilitates the application of HSM. Finally, the future research directions of HMDT in HSM are outlooked.

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.

Tunnel scenes are characterized by rapid light changes, poor lighting conditions, and noise interference, etc. When the intelligent vehicle senses the tunnel environment, it is prone to omission and error detection, leading to traffic accidents. Therefore, for tunnel scenes, a cooperative perception system and dataset based on the fusion of camera and millimeter-wave radar were constructed, carries out research on the problems of poor camera image quality and loss of details due to sudden changes in illumination at tunnel entrances and exits, and proposes an adaptive exposure control model to adjust the exposure time of the camera. The model analyzes the relationship between the number of feature points of different semantic categories in an image frame as a function of exposure time to ensure that the camera can still image clearly under rapidly changing lighting conditions. In addition, for the vehicle-mounted millimeter-wave radar facing the false target problem caused by multipath echo interference in tunnel scenarios, the multipath propagation theory model is built to analyze the characteristics of potential false targets position and energy attenuation in the radar echo, and the multipath false-target elimination strategy is adopted to eliminate the false interference targets. Finally, the corner-point optical flow estimation of moving targets is introduced in the fusion correlation of camera and millimeter-wave radar to improve the reliability of camera and millimeter-wave radar co-sensing, and a real-vehicle platform is constructed to conduct experiments in a tunnel scenario. The results show that the detection accuracy of the proposed cooperative perception algorithm is increased by 4.8% compared with other models, and it has a better vehicle perception performance in tunnel scenarios, which provides an important guarantee for the safe driving of intelligent vehicles in tunnel environments.

Based on the working principle of electrostatic hydraulic soft-body actuators, this paper designs a cable-free soft robotic fish with high underwater maneuverability inspired by manta ray. Firstly, a symmetrical electrostatic hydraulic soft-body drive joint is proposed, which is applied to the pectoral fin propulsion part of the robot fish. The fins are based on the flutter-wave hybrid bionic propulsion mode to obtain the soft robot thrust. Secondly, a buoyancy control mechanism is introduced into the dorsal fin of the robotic fish. By applying positive and negative currents of a certain frequency to the electromagnetic coil, the dorsal fin blade is driven to swing left and right to generate downward propulsion, thereby realizing diving. Finally, the kinematic and mechanical characteristics of the pectoral fin actuator are analyzed experimentally, the dynamic model of pectoral fin flutter under water is established, and the corresponding performance tests and functional integration are carried out. Experimental results show that the soft robotic fish can realize a motion speed of 34.46 mm/s (0.15 BL/s), its turning radius is 15.2 cm, and its diving speed is 14.33 mm/s. The mechanical analysis, drive design and system integration methods of the robotic fish can be generalized to the design of various soft robots and flexible intelligent devices, providing new ideas for the design of next-gen of electrically driven soft robots and underwater equipment.

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.

Value chain collaboration is an important component of Industry 5.0 and a key to realizing the production concept of human-oriented, sustainable, and flexible in Industry 5.0. Data and mechanism, as important technical supports for the integration of the new generation of information technology and value chain, have become key elements in value chain collaboration management. However, there are still problems such as data silos and business barriers in current value chain collaboration, which hinder information sharing and value co-creation among enterprises on the value chain and become a huge challenge for the current transformation of manufacturing to Industry 5.0. In order to provide theoretical support for the application of data mechanism integration in value chain collaboration, this paper summarizes the research progress of value chain collaboration theory and data mechanism integration method, and explores the engineering application of data mechanism integration in five parts, including product design collaboration, production scheduling collaboration, manufacturing assembly collaboration, equipment operation and maintenance collaboration, and production service collaboration. Finally, the challenges faced by data mechanism integration in value chain collaboration are discussed, and the future development direction is looked into. It is hoped that relevant work can provide reference and inspiration for scholars to further carry out theoretical and technical research and engineering application of data mechanism integration in value chain collaboration.

Distributed drive electric vehicles utilize differential torque to generate Direct Yaw Moment (DYM), which effectively improves vehicle maneuverability and controllability. However, DYM is produced by additional longitudinal tire force, which may exceed the tire force constraint region. Misusing DYM could result in hazardous behaviors, such as the vehicle sideslipping in extreme operating conditions. Therefore, it is of high research value to analyze the input boundaries of the optimal DYM and desired driving force to keep the vehicle stable under extreme operating conditions. Considering that both DYM and driving force are related to the driver maneuverability, to this end, a novel concept of driver maneuverability stability region is proposed to describe the feasible operating range of the driver when ensuring vehicle stability, and classifies the vehicle into four modes by distinguishing the response modes of the driver’s desired driving force and the optimal DYM outputted by the upper lateral stability controller. A modal decision criterion is designed based on linear matrix inequality to calculate the boundary of the vehicle stability region and determine the optimal mode. Finally, a multi-modes torque distribution strategy is developed to meet the control requirements under different modes, with full consideration of motor energy saving and motor mechanical fatigue. Simulation and real vehicle experimental results show that the multi-modes torque distribution strategy performs better than the distributed torque distribution strategy and the single-modal torque distribution strategy, which alleviates the contradiction between maneuverability and stability, and ensures the safety and energy saving of the vehicle in handling limit.

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.

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.

Industry 5.0 leads the manufacturing industry to transform towards human-centric intelligent manufacturing. People in different positions and roles in the manufacturing system exhibit more diverse and comprehensive operational, intelligent, and social attributes. Focusing on the typical production scheduling scenario under the human-cyber-physical production system（HCPPS） semantics, an intelligent manufacturing loop is defined that integrates the perception, cognitive, and decision layer. From the three perspectives of adaptive innovation in the integration of operators within the loop, intelligent innovation in the integration of decision-makers on the loop, and sustainable innovation in the integration of social people outside the loop, a multi-level human-centric integration framework is constructed. And it respectively proposes adaptive scheduling technology for the integration of operators, human-machine hybrid intelligent technology for the integration of decision-makers, and sustainable collaborative optimization technology for the integration of social people. Finally, taking the typical scheduling scenario of the aircraft pulsating final assembly line as a case, the effectiveness of the proposed technologies is verified, providing theoretical and technical practical references for the manufacturing industry to achieve human-centric intelligent manufacturing.

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 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.

Structural ceramic materials, characterized by their high hardness, wear resistance, corrosion resistance, high-temperature tolerance, and chemical stability, are widely applied in tribology field. However, as service conditions become increasingly complex, particularly in environments with high temperatures, heavy loads, and intense radiation, the application of structural ceramic friction components still faces significant challenges. Consequently, the industrial sector has set higher standards for the tribological properties of ceramics, making the reduction of friction and wear in ceramic materials a pivotal research topic in materials science and tribology. Due to the complex wear forms and mechanisms of ceramic materials, no universal tribological model currently exists to explain the friction and wear of all ceramics. Therefore, this paper takes silicon carbide (SiC) ceramic materials as an example to review the progress in research on the friction and lubrication of SiC, covering aspects such as wear mechanisms of SiC ceramics, water lubrication techniques, solid lubricating composites, surface engineering technologies, and preparation processes. The aim is to promote the application and development of SiC ceramic materials in tribology while providing a reference for lubrication techniques of other structural ceramic materials.

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.

Heating the batteries at low temperature is an effective way to improve the performance of lithium-ion batteries in extremely cold climates. To address the existing challenges of alternating current(AC) heating, a novel AC self-heating circuit is designed, the circuit simulation and experimental analysis are conducted, and the heating strategies are proposed：A current controllable parallel resonant structure is proposed to address the issues such as restricted application and temperature inconsistency caused by relying on external power sources or multiple batteries in existing heating methods, and a heating circuit model is built, supporting the development of heating strategies; a test platform is built to analyze the effects of parameters such as power width modulation signals, state of charges, frequencies, and number of battery cells in series; according to the test result, heating control strategy based on circuit model and open circuit voltage profile and is proposed, and a switchable heating circuit is proposed, improving the heating performance for battery cells and modules. The results show that the proposed heating method can achieve a temperature rise rate of over 3.1 °C/min at the frequency of 20 kHz and RMS current of 2.7C, and the battery can be heated at the state of charges range of 25%-100%.

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.

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.

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.

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.

Industry 5.0 emphasizes the central role of human beings, and the concept of human-centric is receiving increasing attention in all stages of product design, manufacturing, operation, and service. Conceptual design is the earliest stage of the product life cycle, the core of which is to generate the conceptual solutions that can meet the personalized requirements through the creative activities of the design subject, which greatly determines the level of innovation and the quality of implementation of the subsequent stages. The concept of human-centric puts forward new challenges for conceptual design, and the traditional designer-oriented conceptual design model needs to be transformed into a multi-design subject model in which the designer, user, and machine are in communion. Therefore, this stundy closely integrates the reasoning and decision-making ability of the designer with the deep participation of the user and the computational generation ability of the machine, and proposes a product conceptual design model and framework of user-designer-machine multi-design subject communion under the human-centric perspective, which is aimed to better serve human-centric intelligent manufacturing under Industry 5.0. First, the connotation of human-centric conceptual design is elaborated in terms of influencing factors, forms of expression, and main characteristics. Then, by integrating the human interaction mechanism, human-machine synergy mechanism and design process operation mechanism in conceptual design, a human-centric conceptual design model ‘H（Human）-M（Machine）-D（Design） model’ is proposed, and its basic logic and operation principle are analyzed from the dimensions of users and designers, smart machine, and design process. Finally, an overall implementation framework for human-centric conceptual design is established, and four types of key technologies including cognitive understanding of design thinking, collaborative interaction of design subjects, personalized knowledge services, and conceptual design reasoning and decision-making, are elaborated to provide support for the realization of human-human collaboration, human-object collaboration, and human-machine collaboration in the conceptual design with the communion of multiple design subjects. The human-centered conceptual design process of an elevator is used as a case to validate the proposed model and framework.

The concept of "intelligent nuclear power" technology is proposed to enhance the intelligence of nuclear power plants, with a framework developed based on intelligent sensing and actuating devices, advanced communication networks, intelligent industrial control platforms, and integrated monitoring and management platforms. The application of optical fiber sensors, heterophonic detection, and robotics in nuclear power plants is analyzed in the intelligent sensing and actuators; a scheme for the intelligent nuclear power plant communication network architecture is provided; it also briefly introduces the start up and shutdown sequence control and advanced control technology in the field of intelligent industrial control, as well as functional schemes for the intelligent operator control room. Additionally, combined with intelligent technologies such as industrial internet, artificial intelligence and big data analysis, This paper introduces an intelligent monitoring and management platform based on operation management center, equipment management center, production and management center, briefly introduces the overall architecture of intelligent monitoring and management platform, and describes the main business scenarios applied by each management center. Finally, the paper elucidates the challenges faced by nuclear power plants in future applications of intelligent technology and provides a perspective and summary on the application of intelligent technology in the nuclear power plants.

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.

Transparent antenna using metal mesh has been applied in various fields such as 5G/6G communication, intelligent driving, wearable electronics, and more. With the rapid development of communication technology, the iteration and upgrade speed of transparent antennas has accelerated. Currently, the low-cost, fast, and flexible manufacturing of high-performance metal-grid transparent antennas is the biggest technical bottleneck that restricts their product design, performance verification, and commercial application. In response to this challenging issue, a silver/copper/nickel-based composite metal-grid transparent antenna is proposed, which has the advantages of high conductivity and low conductor loss. Additionally, a composite additive manufacturing method based on electric-field-driven(EFD) micro 3D printing to achieve flexible and rapid manufacturing of multi-material composite metal-grid microstructures is introduced. It enables the flexible fabrication of conductive patterns of arbitrary shapes through EFD microscale 3D printing and combines the volume forming characteristics of electroplating to achieve the rapid manufacturing of high-performance composite metal antennas. Through experiments, the effects of printing and plating process parameters on the precision and quality of formed metal mesh and their patterns were revealed. Based on the proposed method and the optimized process parameters, a transparent microstrip antenna with a center frequency of 2.45 GHz is designed and fabricated with a line width of 20 μm, a period of 500 μm, a square resistance of 0.29 Ω/sq, a transmittance of 78%, and a peak gain of 2.24 dB with an radiation efficiency of 38.26%. The results show that this method overcomes the problems of high square resistance, low gain and radiation efficiency of metal-grid transparent antenna fabricated by conventional processes by forming silver/copper/nickel structures. Moreover, it overcomes the drawbacks of high cost, low efficiency (such as photolithography) and low resolution (such as inkjet printing), and it demonstrates significant advantages in the rapid development of high-performance transparent antennas, showing promising prospects for industrial applications.

Laser welding is widely applied across industries. However, traditional manual teaching or offline programming lacks effective improvements for batch workpiece shape variations. Manual corrections are time-consuming and labor-intensive. In complex welding scenarios like high-reflectivity narrow seams, noise and instability hinder accurate trajectory corrections, affecting quality. A non-rigid registration-based method for correcting welding trajectories on high-reflectivity narrow seams is proposed. Firstly, a positioning method based on dynamic ROI prediction was proposed, which obtains partial weld position points of the actual workpiece through a line laser sensor in a manually guided collaborative manner. Secondly, an optimized WTo-CPD algorithm registers the dense trajectory point set from offline programming to the target point set, creating a new welding trajectory. Finally, the experimental results show that with random errors of 0-0.3 mm, the convergence speed of the WTo-CPD improves by an average of 27.16% and 40.50% compared to Nonrigid-CPD and Bayesian-CPD. The average error is around 0.02 mm and the maximum error is less than 0.21 mm, ensuring the welding quality.