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.

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.

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.

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.

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

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

The 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.

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.

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.

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

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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 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.

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.

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%.

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.

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.

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

Two-stage evaporative organic Rankine cycle(ORC) can effectively improve the efficiency of heat source utilization, and regenerative-ORC can effectively utilize the waste heat discharged by the expander. Combining the advantages of the two, a two-stage evaporative double regenerative organic Rankine cycle system(DEDR-ORC) is proposed. The conventional method focuses on the exergy loss of independent components, and cannot identify the actual impact of the interaction between components on the system performance. The advanced exergy and advanced economic law are proposed to analyze the DEDR-ORC. The advancement of DEDR-ORC, the flow relationship between components and the actual improvement potential of components are discussed. The results show that the exergy efficiency and net output power of DEDR-ORC are 27.24% and 4 436.97 kW higher than those of two-stage evaporation-ORC and regenerative-ORC, and the leveling power cost is reduced by 0.006 . The maximum exergy loss of the expander and condenser obtained by the conventional exergy method is 755.15 kW and 567.20 kW, respectively. The total avoidable exergy loss of the expander, condenser and evaporator 1 obtained by the advanced exergy method is 553.28 kW, 223 kW and 103.20 kW, respectively. The potential for component improvement is sorted according to the total avoidable exergy loss. Therefore, the expander, condenser and evaporator are obtained. According to the advanced economic law, the total avoidable costs of expander, evaporator 1 and hybrid regenerator are 71.16 $/h, 1.14 $/h and 0.94 $/h, which are positive, and the remaining components are negative. The improvement potential of cost reduction is only expander, evaporator1 and hybrid regenerator, and the remaining components have no improvement potential. The research results can provide theoretical support and technical reference for ORC system design and optimization.

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

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

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

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

To address the challenges of inaccurate internal force regulation and uncoordinated human-robot interaction caused by closed-chain coupling and nonlinear hydraulic system characteristics in multi-degree-of-freedom hydraulic dual-arm during human-robot collaborative heavy-load transportation tasks, a hydraulic dual-arm human-robot coordinated control method is proposed for collaborative heavy-load handling. First, based on the analysis of the motion states of the closed-chain system and internal force of the object mapping relationship under human-robot collaboration, the coupled rigid-body dynamics and hydraulic system dynamics in hydraulic manipulators are further decoupled through modular dynamic model, achieving refined modeling of the strongly coupled dual-arm closed-chain system. Subsequently, a human-robot coordinated control strategy is developed by integrating the dynamic compliance of admittance control with an internal force optimization method that minimizes dual-arm contact forces to prevent object damage caused by excessive contact forces while preserving operational intent. Finally, a motion controller is designed base on modular dynamic model as the foundation for the hydraulic dual-arm human-robot coordinated controller. Experimental results show that the dual-arm under human-robot coordinated control not only adaptively adjust object trajectories according to operator intent but also achieve active regulation of internal forces across different directions. Specifically, reductions in internal force errors reach 44.28%-82.46% along the x-axis direction and 44.81%-53.69% along the z-axis direction.

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

In current industrial manufacturing scenarios, robots often suffer from limited perception and lack intelligent interaction strategies, leading to frequent safety accidents, ranging from minor injuries to fatalities. To address the multi-source perception and safety interaction needs in future smart factories, a proactive safety operation concept for industrial robots is proposed, combining on-body and off-body perception. The system is built using a multi-source sensory network, which integrates wide-area visual monitoring, close-range proximity sensing, and collision detection. These technologies process and analyze proximity and contact signals, allowing for the design of shared human-robot control strategies and proactive safety operation planning. Furthermore, after dividing the sensor detection ranges, a Kalman filter-based multi-source fusion method is employed to process the multi-level perception signals and design tiered safety interaction strategies. The proactive safety system developed in this study has been integrated and tested in various platforms of Dongfang electric group, including the shock-type turbine water-wheel robot arc additive manufacturing platform and the large-diameter disc turbine rotor welding platform. The results demonstrate that the proposed multi-source fusion proactive safety method effectively combines the advantages of different sensors in various perception zones, providing perceptual capabilities and safety responses for human-robot interaction in full-space and full-scenario environments. This significantly enhances the safety and operational capability of industrial robot systems.

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

In human-robot collaborative disassembly, manufacturing systems predominantly rely on fixed perception-cognition paradigms governed by pre-established algorithms. This reliance poses significant challenges in accommodating the flexible requirements of operators, which are inherently informed by experience and the dynamic collaborative environment. As a result, robotic path planning often fails, and decision-making stalls. To address this, an embodied augmented reality disassembly system for human-robot-environment integration is proposed. The system is grounded in embodied intelligence theory and features a "perception-cognition-execution" mechanism. By combining this mechanism with augmented reality technology, it enhances environmental perception and cognitive reasoning. A collaborative disassembly strategy for embodied augmented reality is designed; a local image attention model with context enhanced mechanism to generate adaptive image captioning; a self-optimizing cognitive reasoning method is developed by large language model tuning and inference mechanism; a robotic manipulation method is developed through augmented reality-based human-robot-environment data interaction. Three similarity metrics are constructed to evaluate the performance of embodied perception and cognition. Quantitative and qualitative experiments demonstrate the system’s feasibility and effectiveness in enhancing human-robot collaborative disassembly efficiency and adaptability.