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.

To achieve precise and automated segmentation of the middle section of pork carcasses， an improved YOLOv11-Pose-Based keypoint detection method for rib contour recognition is proposed. Addressing the limitations of existing algorithms in recognition speed， learning efficiency， and detection accuracy， the CBAM attention mechanism's CAM submodule is innovatively replaced with the Bi-level routing mechanism from Biformer， effectively enhancing model efficiency and recognition accuracy. The experimental results demonstrate that the improved model achieves significant performance gains across multiple metrics. For keypoint detection， the mAP（0.5） reaches 0.995， representing a 2.37% improvement over the baseline model， while the mAP（0.5-0.95） increases by 0.97%. The precision of bounding box prediction improves to 0.983， an enhancement of 2.82%. Additionally， the recall rate rises to 96.5%， a 7.8% increase compared to that of the original model. Meanwhile， the training time is reduced by 8.46% without altering the model size， significantly enhancing computational efficiency. The visualization results confirm that the improved model accurately identifies the rib contour of the middle pork carcass section and precisely predicts five key points， providing reliable technical support for subsequent automated segmentation tasks. The findings indicate that this approach achieves high accuracy while improving computational efficiency， offering a novel solution for intelligent pork segmentation technologies.

The application scenarios of large-scale equipment data communication in industrial sites requires a data communication solution with low latency， large capacity and high speed. This paper compares mainstream cellular IoT technologies， proposes an industrial site data collection technology solution based on the 5G lite technology RedCap， desigs a data communication terminal based on 5G RedCap technology， and verifies its feasibility through testing. The data communication terminal based on 5G RedCap technology can well meet the needs of industrial site data communication， is in a valuble position to be promoted and applied in the field of IIoT， and will push forward the development and evolution of cellular IoT towards end-network collaboration.

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.

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.

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.

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.

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.

This paper addresses the challenge of inadequate timeliness and accuracy in processing multi-source heterogeneous data within special equipment assembly workshops， which tends to hinder real-time transparent management and control during the assembly manufacturing process of specialized equipment. To tackle this， a method for fusing multi-source heterogeneous data in these workshops is proposed with the approach based on an analysis of the composition and characteristics of operational data in these workshops， and the benefits of Multi-Agent technology is applied to construct a framework for data fusion. The research on the methods involved in data layer fusion and feature layer fusion is also conducted. The feasibility and effectiveness of the proposed method are confirmed through simulation examples， thereby providing reliable， timely， and accurate data support for the intelligent operation and control of special equipment assembly workshops.

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.

To ensure the safety and stability of the power transmission systems and achieve comprehensive inspection and maintenance of newly constructed power transmission towers， this paper proposes a centrally symmetric quadrupedal humanoid climbing robot designed for existing foot pegs used by maintenance workers to climb towers. Each limb is configured with 3-1-2 arrangement. At the end of each limb， a large-tolerance， semi-enclosed hook-type gripping tool is designed specifically for the foot pegs. This tool features high tolerance， eliminating the need for precise end-effector positioning and enabling rapid engagement with the foot pegs. Humanoid climbing gait planning method is developed， facilitating the robot's full-range climbing of the power transmission tower by quickly hooking and gripping the foot pegs using the hook-type tool. Targeting a 40-meter-high self-supporting transmission tower， the robot's full-range climbing dynamics model and simulations were completed. Simulation results demonstrate that the proposed robot configuration can achieve humanoid full-range climbing of the tower， with a climbing time from the base to the top of less than 30 minutes， matching the efficiency of maintenance personnel. This provides a feasible solution for robotic maintenance applications in power transmission towers.

To solve the problems of small molding size and low printing efficiency of traditional desktop-level single-nozzle FDM 3D printer， a medium-sized FDM multi-nozzles collaborative 3D printer is designed and built. The printer adopts a Cartesian （XYZ） structure and is equipped with three side-by-side composite printing nozzles， and the materials can be selectively extruded according to the demand. The control system is divided into three parts according to the functions： main motion control module，embedded auxiliary measurement and control module and upper computer software module，while the software and hardware of these three parts are developed.Two printing modes of multi-nozzles synchronous forming and multi-nozzles stackable co-filling are designed and the corresponding path planning algorithms are proposed.After printing verification， compared with single-nozzle printing， the synchronous forming efficiency of the composite multi-nozzles printer is increased by 3 times， whereas the stackable co-filling printing time is reduced by 41%. The printing efficiency is significantly improved under the premise of ensuring the printing quality.

Heat exchangers play a crucial role in improving the energy efficiency of industrial processes， reducing fuel consumption， and decreasing greenhouse gas emissions. This paper addresses the innovative design problem of heat exchangers with numerous parameters， variable structures and complex medium flow characteristics by proposing a generative design method for spiral tube heat exchangers. Firstly， it analyzes the design principles， the structural advantages， and the performance characteristics of spiral tube heat exchangers， introduces the application process of the generative design method， the design optimization logic， and the automated parametric model generation method. Then， through computational fluid dynamics simulation， it evaluates the thermal transfer efficiency and fluid dynamics performance advantages of the spiral tube heat exchangers. Finally， through structural mechanics simulation， it assesses the risk resistance performance advantages of the spiral tube structure under various operating conditions. The proposed generative design method achieves rapid optimization iteration of design solutions and rapid generation of heat exchanger models， providing the possibility for rapid exploration and design of high-performance spiral tube heat exchangers.

Detection on the surface defect of Pouch Cells is a key procedure of the production process. Aiming at the problems of low detection accuracy and difficult imaging of large-sized batteries in the existing detection methods， a detection method based on photometric stereo imaging and deep learning was proposed. Firstly， a Multi-Source Time-Sharing Exposure Imaging System （MSTIS） was established by combining photometric stereo and line scan camera imaging technology. After obtaining the surface images of batteries under different light sources through time-sharing exposure， photometric stereo calculation was conducted to obtain the curvature map with 3D information. Then， to solve the problem of missed detection of minor target and multi-scale defects， the YOLOv8 algorithm was improved. An edge information enhancement module （EIEM） was developed using a dual-channel convolution structure， which incorporated Sobel convolution and conventional convolution to improve feature edge extraction capabilities. The semantic and detail information fusion method （SDI） was integrated with the bidirectional feature pyramid module to boost the recognition accuracy of tiny defects. A lightweight shared convolution detection head was also implemented to reduce the algorithm's computational load.The experimental results show that the average detection accuracy of this method reaches 94.2% and the detection speed reaches 116 FPS， which can effectively detect the surface defects of pouch cells.

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.

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.

Accurate force/position control in the presence of modeling uncertainty in robots is a challenging task. This article is based on improved integral sliding mode control for hybrid visual & force control. To perform visual servoing， an optimized visual feature is adopted to avoid the ill-condition visual Jacobian matrix. An improved super-twisting algorithm is proposed based on time delay estimation for integral sliding mode control， and an improved sliding surface and convergence law is used for the positive definite part of integral sliding mode control. Correspondingly， visual control method and force control method are proposed to apply to the hybrid visual & force control framework. To avoid the impact of noise from the force control part on the control output， a visual admittance framework is used for hybrid control. To optimize the solidification impedance characteristics caused by fixed admittance parameters， a fuzzy adaptive admittance framework is proposed to inherit the advantages of admittance while adaptively adjusting parameters in real-time. Finally， a 6-degree-of-freedom deviation model is used for simulation to verify that the proposed scheme can accurately track visual trajectories and expected forces under relatively small chatting. The performance of the three with different focuses is compared based on direct visual perception hybrid control， visual admittance control framework， and fuzzy adaptive admittance framework. Under the deviation model， the proposed i-CISMC algorithm is validated by surface force tracking to have better tracking accuracy while ensuring smaller control chattering； the proposed algorithm is combined with the fuzzy adaptive admittance framework， and the results of tracking under noise have proved that the framework inherites the suppleness of the admittance framework to the disturbance of the force loop， and at the same time， can adaptively change the parameters of the admittance to obtain better tracking speed and tracking accuracy.

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.

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.

To address issues such as low detection accuracy， slow speed， missed and false detections， and large model parameter sizes in complex scenarios from a UAV perspective， this paper proposes an improved RBGE-YOLO algorithm model. Firstly， RFAConv is introduced in the backbone network to replace the original Conv， enhancing the model's ability to extract and fuse image features. Secondly， the neck network is reconstructed using BiFPN-GLSA to improve feature fusion and spatial feature utilization efficiency. Thirdly， a dual-layer small target detection structure is designed to strengthen the feature information of small targets. Finally， the Inner-EIoU loss function is utilized to address the limitations of IoU. Experiments on the VisDrone2019 dataset show that RBGE-YOLO improves Precision， Recall， mAP@0.5， and mAP@0.5：0.95 by 4.7%， 2%， 3.6%， and 2.5%， respectively， compared to the original YOLOv8s， while reducing the number of parameters by 16.4%. This achieves model lightweighting while significantly enhancing detection performance.

To address the challenges of frequent bearing failures under complex working conditions， as well as the low real-time performance and strong dependence on manual feature extraction in traditional diagnostic methods， this paper proposes a bearing fault diagnosis method based on a deep learning model combining a Multi-Scale Convolutional Neural Network （MSCNN） and Long Short-Term Memory （LSTM）， and develops an intelligent bearing health management system. The system adopts an end-to-end diagnostic workflow， directly taking raw time-domain vibration signals as input. It extracts hierarchical local features across different frequency domains through MSCNN， and captures the temporal evolution of fault characteristics using LSTM， thereby achieving high-accuracy automated fault classification. To enhance the interpretability of diagnostic results and support intelligent maintenance decisions， the system integrates the Chinese large language model iFLYTEK Spark， which generates natural language diagnostic reports and maintenance suggestions through standardized prompts. The system is deployed on a domestically developed Phytium quad-core processor platform， ensuring full autonomy and reliability of both hardware and software components for industrial applications. Experimental results show that the proposed system achieves an average classification accuracy of 98.46% on the CWRU bearing dataset， and 96.73% on the AITHE bearing fault dataset， demonstrating strong robustness and cross-dataset generalization under complex and noisy conditions. With real-time visualization of diagnostic results and maintenance recommendations through a human-machine interface （HMI）， this system provides a reliable and intelligent solution for equipment health management and predictive maintenance.

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.

With the rapid development of FDM 3D printing technology， the demand for printing efficiency and molding accuracy is increasing cross various industries. This paper proposes a motion system control strategy based on a combination of BP neural network and PID control algorithm to address the issue of insufficient forming accuracy in Delta structured FDM 3D printers. By introducing BP neural network into the motion control system of the printing device， dynamic adaptive adjustment of control parameters has been achieved， effectively improving the stability and accuracy of the printing process. The experimental results show that this control strategy significantly reduces printing errors and improves the surface quality and dimensional accuracy of the molded parts. This study provides a new solution for improving the accuracy of Delta structured FDM 3D printers， which is of important theoretical significance and practical application value for promoting the development of 3D printing technology.

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

The 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.

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.

To address the issues of low spatial utilization and Inbound and outbound efficiency in cargo location allocation for mechanical processing warehousing scenarios， a hybrid model （GA-XGBoost） integrating Genetic Algorithm （GA） and XGBoost parameter optimization is proposed. By constructing a two-layer encoding mechanism for feature selection and hyperparameter collaborative optimization and combining an improved greedy algorithm with dynamic priority adjustment， a multi-constraint decision model is established with optimization objectives of spatial utilization， inventory time， and prediction accuracy. The experiments based on warehousing data of 500 cargo locations and 1，200 types of goods show that the average in/out time is reduced to 17.9 minutes， representing an 18.7% efficiency improvement； the mean squared error of prediction is reduced to 0.012， and the number of convergence generations is decreased by 19.4%. This method effectively balances multi-objective constraint relationships and provides a dynamic cargo location allocation scheme for intelligent warehousing systems that coordinates high-density storage and efficient operations.

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.

To address the problems of scattered knowledge systems in ship assembly and ineffective mining and utilization of massive process data， this paper proposes an automatic knowledge graph construction technology for the shipbuilding domain based on large language models. This method uses large language models to convert unstructured and semi-structured ship data into structured data to build a ship process corpus. It models ship ontology knowledge structure with the assistance of large language models， designs an instruction prompting framework for ship assembly domain， and achieves efficient entity-relationship extraction， to complete the automatic construction of knowledge graphs. Additionally， the method uses triple sets constructed by general large language model instruction prompts as fine-tuning training sets to further fine-tune specialized small language models， ensuring the security of specific private ship data while reducing computational resources. Experimental results show that this method outperforms traditional baseline models in key metrics such as accuracy， providing a new technical approach for knowledge management and intelligent upgrading in the shipbuilding domain.

In the field of modern logistics warehousing， carton identification is crucial for inventory management and logistics automation. Aiming at the shortcomings of traditional methods and some existing automation schemes in carton detection tasks， a new carton detection method based on improved YOLOv8 network is proposed. Firstly， an Adaptive Batch Normalization（ADBN） mechanism is proposed and introduced to the YOLOv8 backbone network， enhancing the feature extraction ability. The C2f-Faster-CGLU mechanism combining FasterBlock and Convolutional Gated Linear Unit （CGLU） is introduced into the YOLOv8 detection header， which improves the computational efficiency. In addition， a new boundary frame similarity comparison index based on the minimum point distance （MPDIoU） is introduced， which can evaluate the similarity between prediction and real frame more accurately. Finally， the improved network model is applied to Rectagular Stacked Carton Dataset（RSCD）， Online Stacked Carton Dataset（OSCD） and Live Stacked Carton Dataset（LSCD）. Compared with that of the original model， the mAP of the improved model is increased by 1.6%， and the recall rate is increased by 1.3%. The improved model has also improved performance compared with other mainstream detection algorithms，providing more accurate and efficient technical support for the object detection of modern logistics and warehousing industry.

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.

Aiming at the problem that traditional convolutional neural networks cannot effectively extract global features and some deep learning models are more complex， this paper proposes a lightweight gearbox fault diagnosis method based on multi-scale convolutional neural networks and feature fusion ViT. Firstly， a multi-scale feature extraction module is constructed， which captures the feature information of the data from multiple scales by multi-scale convolutional neural network using different scale convolution kernels， and fully exploits the local features of the input information. Then， the feature fusion ViT module is designed， which utilises an improved multi-attention mechanism to capture the global features of the fault information， and further constructs the D-MLP to reduce the number of parameters in the model using depth-separable convolution. Finally， the experimental validation is given using gearbox data from Southeast University， and the results show that， compared with the comparison methods， the proposed method has high fault diagnosis accuracy and good generalization ability under complex conditions such as variable operating conditions and variable noise.

Most amphibious biomimetic robots encounter problems such as insufficient motion ability， poor environmental adaptability and low simulation rate. This article adopts a novel central pattern generator （CPG） with dual neuron mutual inhibition as its main controller based on the basic rhythmic gait of salamanders， and ensures the phase coupling relationship between adjacent CPG units by adjusting the excitation suppression parameters between each neuron. Based on this， a salamander robot spinal cord like control neural network is established. The neural network consists of two layers：Interneuron and Motor neuron. The Interneuron layer generates rhythmic signals， which are then integrated by the Motor neuron layer before outputing to the joint muscle model to drive the robotic movement The performance of spinal cord control network was simulated and analyzed by combining Simulink and Webots. The simulation results show that the amphibious salamander biomimetic robot can effectively achieve rhythmic gait such as swimming and land crawling. The neural network for motion control of the salamander robot designed in this paper is feasible and effective.

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.