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.

Due to the combined effects of manufacturing errors, positioning errors, contact deformations, and inconsistent surface qualities, the assembly contact forces exhibit random disturbances, leading to issues such as jamming, non-compliance with process requirements, and even component damage in contact-rich automated assembly. Recent research has shown that employing learning-based approaches for assembly contact control is one of the most effective strategies to address contact-rich automated assembly problems. Considering the significant progress made by reinforcement learning methods in contact-rich robotic assembly, this paper analyzes and statistically characterizes assembly features with contact-rich characteristics in the field of robotic automated assembly. It proposes discriminative indicators for identifying contact-rich assembly situations. Through an analysis of relevant literature in the field, the methods for learning contact force control in robotic automated assembly are categorized into three main types：reinforcement learning-based contact control methods, reward-engineered contact control methods, and simulation-to-reality contact control methods. Each of these categories is reviewed and analyzed. Finally, an analysis and outlook on the future development trends of learning contact-rich robotic automated assembly control skills is provided.

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.

Gear transmission system has also become an indispensable component in the equipment manufacturing industry. With increasingly complex service environment of equipment in ocean engineering, aerospace, transportation, energy and other fields, extreme working conditions with high speed, heavy load, and drastic disturbances occur more frequently than before, resulting in a surge in vibration and noise in the gear transmission system, and threatening the overall reliability, comfort and concealment. Therefore, the dynamic performance of gear transmission system has become the key to promoting the operating performance of the system and the entire machine, and there is an urgent need to design the gear transmission system according to the dynamic performance. Based on an extensive survey of domestic and foreign research results, the existing dynamic performance analysis methods of gear transmission system are summarized, and a number of internal and external excitation factors that affect the dynamic performance are sorted out. Especially, the impact of multi-source excitations and uncertainty on the dynamic performance is emphasized. The optimization methods towards the dynamic performance with single/multiple objective(s) and those considering uncertainties and robustness are also summarized. The applications of modern dynamic performance design methods are also introduced from the aspects of marine ships, railway, wind turbine, robots, aerospace, etc. This work is not only beneficial for constructing a comprehensive and intelligent system for dynamic performance design, but also helpful for improving the vibration, noise and other synthetical characteristics of gear transmission system.

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.

Urgent thermal protection needs exist for the new generation of aerospace equipment, and there is an urgent need to develop ultra-high temperature ceramics with excellent thermodynamic, oxidation, and ablation resistance properties. Among them, the carbide ultra-high temperature ceramic system, which has the most outstanding thermal properties, has shortcomings in mechanics and oxidation resistance. Starting from the structure and properties of carbide ultra-high temperature ceramics, this review summarizes the enhancement effects of strengthening-toughening designs, including toughening phase introduction and microstructural bionization, on the mechanical properties. The entropy-enhancing research to modulate its structure and properties is introduced, covering cationic solid solution, anionic modification, and high-entropy design. The main construction methods of carbide ultra-high temperature ceramic thermal protective coatings are sorted out, and the oxidation and ablation resistance properties and mechanisms of the resulting coatings are summarized. Finally, the main development directions of carbide ultra-high temperature ceramics are outlined in terms of material computational design, synergistic enhancement by strengthening-toughening and entropy-enhancing, ablation property and mechanism, and preparation of large-size components and coatings.

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.

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.

A systematic review is conducted on the detection principles of rail corrugation, with advantages, limitations, and applicable scope summarized. Detection methods based on different principles are elucidated, and an overview of common static and dynamic detection devices for corrugation is provided, along with research progress on next-generation detection and monitoring devices. Research prospects for corrugation detection are discussed. Detection principles for rail corrugation are categorized into five types: chord measurement method, inertial reference method, signal reconstruction method, machine vision method, and time-series modeling method. Inherent drawbacks, such as non-unit transfer function and severe amplitude oscillation for shorter wavelengths, are associated with the chord measurement method. Corrugation can be measured effectively using a combination of chord models. The inertial reference method, based on inertia, can be installed at multiple positions, such as axle boxes, frames, and the car body. At low speeds, inertial sensor responses diminish, while noise and trend components gradually dominate. Digital signal processing techniques are used by the signal reconstruction method to decompose data from sources like axle box acceleration, frame acceleration, wheel-rail noise, and in-car noise, extracting valuable information about corrugation. Rail images are perceived, understood, and interpreted by computers using the machine vision method, which is based on image processing and pattern recognition technologies. This method mainly comprises three approaches: image processing-based, laser camera-based, and 3D point cloud reconstruction-based methods for corrugation measurement. Corrugation recognition is transformed into classification or regression problems by the time-series modeling method. Through machine learning and deep learning techniques, mapping relationships between corrugation and responses like vibration and noise are established, achieving corrugation detection. Detection and monitoring devices for rail corrugation are advancing towards intelligent, integrated, and portable measurement solutions.

To address the significant demand for efficient, high-quality repair and machining of surface defects of complex components in high-end equipment manufacturing fields such as transportation, aerospace, energy and defense, the research progress in recent years on robotic repair and machining technology is reviewed. This research systematically analyzes the relevant literature published at domestic and international level around the key technologies of defect visual measurement, path decision planning, and machining quality control involved in robotic repair. It also describes the engineering applications of robotic repair by taking automotive body, high-speed rail body and turbine blade as examples. Finally, the future research directions of this field are envisioned from the aspects of multi-robot collaboration, online information interaction, dynamic performance monitoring, and hybrid machining process.

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.

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.

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.

With the improvement of the surface performance requirements of key components in industrial machinery and equipment in extreme environments, Ni-WC functionally graded material(FGM) shows more extensive application potential in improving the surface performance of key components. In this study, Ni-WC gradient composite coatings are prepared on H13 steel substrate by Ultrasonic-assisted laser cladding(UALC). By controlling the mixing ratio of WC particles and Ni60 powder, three coatings with different WC contents (WC15, WC25, WC35) are successfully constructed. The introduction of ultrasonic vibration significantly optimizes the microstructure of the coating, reduces internal defects such as cracks and pores, and improves the compactness and uniformity of the coating. The results show that gradient design and ultrasonic assistance can effectively reduce the internal cracks and pores of the coating and improve the forming quality of the coating. Ultrasonic assisted promotes material exchange between layers, reduces stress concentration and performance differences; the hardness, wear resistance and impact resistance of the gradient coating assisted by ultrasonic are improved. Compared with the traditional multi-layer single WC25 coating, the hardness is increased by about 10%, the wear rate is reduced by 55.56%, and the impact absorption work is increased by 18.7%. The ultrasonic gradient coating exhibits the best impact toughness, and the fracture analysis shows that it has more dimples. The research results confirm the effectiveness of UALC technology in improving the performance of gradient coatings, and provide a new technical approach for surface strengthening of key mechanical components.

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.

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.

Numerical control machine tools, as the core equipment in high-end manufacturing, are crucial and fundamental to modern manufacturing development. Traditional machine tool manufacturing mainly focuses on the “functional possibility”, i.e., satisfying the basic functionalities of NC machine tools. However, with the rapid advancement of intelligent manufacturing and high-end equipment, simply meeting functional requirements can no longer address the challenges of increasingly complex manufacturing environments. Therefore, this paper proposes a novel paradigm for reliable manufacturing of NC machine tools, shifting the focus from “functional possibility” to “performance reliability”. Based on key life-cycle technologies, including forward reliability design, reliability process control, an optimized reliability testing system, and in-service health management, this paradigm establishes an innovative, multidisciplinary collaborative mechanism to systematically enhance the performance reliability and stability of machine tools. It aims to provide new theoretical guidance and technical pathways for the NC machine tool industry, thereby supporting the sustainable development of high-end equipment manufacturing.

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.

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.

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 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.

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.

18CrNiMo7-6, as the main material for the gear of high-end equipment such as offshore wind turbines, heavy mining equipment, large warships, and high-speed trains, its bending fatigue performance is an important bottleneck that limits equipment reliability, fatigue life, and power density. However, the specific impact of processes such as grinding and shot peening on the bending fatigue strength of gears is not yet clear, and the conversion between bending fatigue limits at different reliability levels has not been discussed, significantly restricting the lean design of high-performance gears. The bending fatigue tests of 18CrNiMo7-6 gears using a series of processes including carburizing and grinding, ion implantation, fine particle peening, barrel finishing, shot peening, tooth surface plus end face shot peening, and dual shot peening were systematically carried out. More than 700 effective bending fatigue test points were obtained, with a test duration of over 7 000 h. In addition, and a prediction formula for the bending fatigue performance of gears of this material grade was proposed. The results show that within the studied range of processes, the bending fatigue limit range of 18CrNiMo7-6 gears is 482-762 MPa; the conversion coefficient range of bending fatigue limits for 50% reliability and 99% reliability ranges from 0.841 to 0.965, with the recommended value of 0.929. The bending fatigue limit value of gears is mainly related to processing technology, module, specific processing environment of the enterprise, etc. The average absolute prediction error of the bending fatigue limit prediction formula for gears considering the surface integrity parameters is 6.19%, providing valuable research methods and data support for the development of high power-density gear transmission in engineering practice.

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.

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.

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.

To improve the adaptability of small mobile robots in complex environments, an innovative wheel-leg hybrid robot with passive transformable wheels is proposed, which retains the steady motion and simple control of conventional wheeled robots. The wheel’s configuration can transforme passively relying on the constraint force between the wheel and obstacle terrain with a transformation ratio of more than 2.3. A force analysis is conducted during the wheel’s transformation and restoration process, and multi-objective optimization design method is used to optimize the wheel’s structural parameters with the goal for transformation capacity and maximum obstacle climbing height. Based on the passively transformable wheels, the robot can realize active and passive switching between wheeled and legged mode based on changes in the friction state between the wheels and terrain, or by controlling the relative speed of the front and rear wheels. By establishing a mechanical model for robot obstacle climbing, the influence of robot parameters on obstacle climbing stability is studied, and the results show the robots can climb relatively high obstacle relying on wheel’s transformation.

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.

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.

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.

Model predictive control(MPC) has many advantages. However, the complexity of the optimization algorithm arises due to the multi-constraints and nonlinear character of its dynamic model when applied to yaw stability control in intelligent vehicles, making it challenging to achieve sufficiently short control cycle time and step. Therefore, an online explicit solution method for intelligent vehicle yaw stability control based MPC is proposed. It uses Taylor expansion to convert nonlinear model predictive control(NMPC) to linear time-varying model predictive control(LTV-MPC), and then designs a rolling adjustment weight coefficient to convert inequality constrained optimization to unconstrained optimization that can be directly and explicitly solved, so as to avoid multi-step iterative optimization and speed up computation time of MPC. The simulation results indicate that the proposed explicit solution can increase the solving speed of MPC by 3-4 times while ensuring the same control effectiveness, which can significantly improve the real-time performance of MPC in vehicle yaw stability.

Roll forming is a material-saving, energy-saving, high-efficiency sheet metal forming process, which has been widely used in the construction industry, automotive manufacturing, transportation and many other fields. At present, the research and production of roll forming technology are mainly based on the traditional uniform section shape, but the arrival of Industry 4.0 puts forward new requirements and trends for the future development of roll forming. In order to improve the scope of application of roll forming and meet higher product requirements, some technologies such as flexible roll forming and hot roll forming continuously developed and researched. More efficient, precise and flexible roll forming equipment comes into being with the in-depth study of cutting-edge technology. The research and development status of the theory research, process research, advanced technology, equipment development and intelligent manufacturing of roll forming technology are reviewed. In addition, the advanced development of roll forming in various application fields in recent years are presented, and the key problems and breakthrough points of roll forming are pointed out.

Deep space exploration and detection of the deep sea require mechanical parts to face more complex environmental tests and have a longer and more reliable operational lifetime. Solid lubrication not only improves the stability of the operation of mechanical components but also extends their service life. Diamond-like films have become one of the most popular lubrication materials due to their unique combination of properties, including low coefficient of friction, wear resistance, high hardness, biocompatibility, and chemical inertness, and their tribological properties are highly dependent on the application environment, structural characteristics, and mechanical properties of the film. A review of the changes in tribological behavior of DLC films in ground atmospheric environments, marine corrosion environments, and space irradiation environments; the energy dissipation mechanism under different friction conditions is analyzed; the evolutionary law of the structure-performance relationship is discussed; and, finally, a reliable method to improve the environmental adaptability of DLC films is proposed. In addition, the expected of the development trend of DLC film points to the diversification and standardization towards adapting to harsh and changing environments.

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.

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.

The printing accuracy of continuous fiber composite directly affects the external dimensions and assembly accuracy of components. This study focuses on the package structure and honeycomb filling structure of conformal phased array antenna, and conducts a study on the printing accuracy of continuous fiber reinforced composite. Firstly, a force model of continuous fiber filament during the printing process is established, and the force situation of the filament during the printing process is analyzed. Combined with the force situation, the accuracy loss mechanism of arc, straight line, and right-angle features during the printing process is studied. Subsequently, the influence of process parameters on the printing accuracy of arc and right-angle features is studied, and finally, the typical package structure and honeycomb filling structure are printed successfully. The research results indicate that printing accuracy is mainly controlled by the friction force and adhesive force on the filament. When the friction force is greater than the adhesive force, it is easy for the filament to slip or detach. The minimum printing radius of an arc is influenced by both printing speed and layer thickness. The parameter combination with a layer thickness of 0.3 mm has a higher ability to form large curvature arcs. The length loss and area loss of printed right-angle are influenced by both printing speed and layer thickness, and they both increase with the increase of printing speed and layer thickness. The parameter combination with a layer thickness of 0.3 mm has a high ability to stabilize printing accuracy.

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%.

A game-based interactive decision-making and trajectory planning algorithm is proposed to address the problem that overly conservative or aggressive lane-changing behaviors will be exhibited by intelligent vehicles in complex dynamic scenarios due to lake of effective interaction with surrounding vehicles. A human-driven vehicle model is established based on driver operation characteristics and driving styles, by utilizing Bayesian inference algorithms to assess the conservativeness of driving behavior and enabling real-time prediction of interactive vehicle maneuvering actions. On this basis, a decision-making and planning model for lane-changing maneuvers of the ego-vehicle is developed. Initially, lane-changing endpoints are sampled to generate candidate lane-changing paths. Subsequently, an ego-vehicle reward function is constructed to evaluate lane-changing strategies during the game process. Finally, a dynamic programming algorithm is designed, which incorporates the idea of leader-follower games and fully considers the generation of optimal strategies and trajectories in interactive gaming scenarios. The human-in-the-loop simulation platform, built with Matlab/Simulink and PreScan, is utilized to validate and analyze multiple lane-changing scenarios. The results indicate that the algorithm effectively interacts with surrounding vehicles in complex and dynamic lane-changing scenarios, demonstrating rational behavioral decisions and producing trajectories that prioritize both safety and smoothness.