In recent years, various deep learning-based health management models for mechanical equipment have made significant progress. However, existing models tend to be smaller in scale and are typically designed to handle data from specific frequencies, speeds, or modes, focusing on particular components such as gears and bearings to perform tasks like monitoring, diagnosis, and prediction. These models struggle to adapt to new scenarios and lack the capability for continuous evolution. With the increasing precision and complexity of high-end equipment, there is a growing demand for highly general, scalable, and evolvable "one-stop" health management services. Inspired by the trend of generalization in large language models like ChatGPT, which excel in handling diverse data, tasks, and scenarios, a large model for general prognostics and health management of machinery is proposed. First, multimodal data is resampled in the angular domain and segmented to token sequence. Then, the data is input into a Transformer-based information integration foundational model to extract health and degradation information into specific tokens. Finally, these specific tokens are used to perform downstream tasks such as monitoring, diagnosis, and prediction. The proposed large model's baseline performance, multitask synergy, and scalability were verified using fault and long-term degradation datasets. The results show that the proposed large model can simultaneously perform condition monitoring, fault diagnosis, and remaining useful life prediction for multiple objects like bearings and gears. Additionally, the diagnostic and predictive multitasks can effectively collaborate, mutually enhancing performance, and achieving better results compared to single-task models. In few-shot learning and continual learning scenarios, the large model can be rapidly deployed and continuously evolved. Therefore, the proposed large model features high generality, scalability, and sustainability, and is expected to provide universal "one-stop" health management services for mechanical equipment.

As a micro-electromechanical system with a size of centimeters or below, micro-bionic robots have the characteristics of small size, light weight and excellent portability. They are widely used in complex environments such as environmental detection, target search, reconnaissance and strike. In order to enable researchers to understand the research progress of micro-bionic robots, a summary and analysis of relevant literature in the past 15 years are conducted based on the world’s largest abstract and citation database Scopus, providing a visual depiction of the development trends in the field of micro-bionic robots. The general characteristics and research status of micro-bionic robots are summarized from the three key points of the bionic movement form, manufacturing technology and driving technology of micro-bionic robots, supplemented by the introduction of the special research direction of bio-electromechanical hybrid micro robots. The technical bottleneck of the development of micro-bionic robots is fully analyzed, and the development idea of energy-driving-sense-control full flexible integration is put forward, which promotes the innovative development of integrated manufacturing technology. Based on the military and anti-terrorism and riot control application background, the characteristics and advantages of micro-bionic robots are fully analyzed, and the combat application conception with micro-bionic robots as the core is carried out. In addition, the application of micro-bionic robots in civil life is discussed. Finally, the shortcomings and future development of the existing micro-bionic robots are discussed and summarized, which provides a valuable reference for the development of the micro-bionic robots technology and its military application prospect.

How to effectively integrate various sensor measurement information in complex systems to form the basis for intelligent control of the system is currently a hot research topic in the field of intelligence. Once the traditional multi-sensor data fusion structure is determined, it cannot be changed which lacks sufficient elastic computing power to ensure the addition of other sensors, and also hinders the construction of the system’s multi-sensor network. In order to form a sensor network to gather comprehensive system data, firstly, a data flow structure based on a data pool is designed on the architecture. So the sensor measurement information is aggregated in the data pool, and related algorithms are treated as services, solving the problem of elastic computing power in data processing; Secondly, the data processed by the service is pushed to the control system according to demand, separating data processing from control, forming a star shaped sensor network centered on the data pool; Finally, an universal non-model data fusion algorithm framework based on detection signals was designed. Which the coefficients after signal decomposition were fitted, and the reconstructed signals were used for state correction, completely eliminating the dependence on the system model and greatly reducing the impact of sensor entering control system. At last, summarized the feasibility and effectiveness of the method from the perspective of design philosophy, structural evolution process, and characteristic analysis. The proposal of this method can provide a new approach for the research of intelligent control of complex systems.

Integrative design and integrated manufacturing of major equipment’s’ large critical structure such as aeronautics, astronautics and weapons provide guarantees of lightweight manufacturing and service performance. As a transformative technology can achieve innovative structure, additive manufacturing has received extensive attention and being applied, nevertheless, additive manufacturing of lightweight and high-strength metals such as high strength aluminium alloy and magnesium alloy faces many challenges. Additive friction stir deposition provides a new thought and method for such kind metals, since its process involves strong plasticity and non-melting, which further facilitates the progress of solid-state additive manufacturing and equipment. Dominant advantages of additive friction stir deposition have aroused worldwide attention and investigation; However, this technology’s basic theory and deposited materials’ microstructure evolution and performance need to be clarified. Research progress in additive friction stir deposition was systematically summarized, domestic and foreign research achievements such as heat production mechanism, material flow behavior, design of printing tool, processing parameters, microstructure evolution and performance of additive friction stir deposition were comprehensively reviewed. Finally, future opportunities and development trends of additive friction stir deposition were pointed out.

In the transition from Industry 4.0 to Industry 5.0, a human-centered approach has gradually emerged as a focal point in the field of smart manufacturing. Current human-machine collaboration not only emphasizes technological advancements and efficiency improvements but also stresses the integration of human higher-order cognitive thinking with machine computational capabilities to achieve cognitive empowerment. Based on this premise, this study reviews existing research on cognitive empowerment in human-machine collaboration, focusing on key areas such as interactive perception, task planning and execution, and skill learning. The challenges of multimodal information integration, task reasoning, dynamic decision-making, and skill knowledge representation are highlighted. Furthermore, methods are proposed to support human-machine cognitive using knowledge graph construction technologies, as well as to optimize tasks and facilitate dynamic decision-making in complex environments through the application of knowledge graph reasoning techniques. Building upon an analysis of the limitations in current research on cognitive empowerment in human-machine collaboration, this study also forecasts the future directions for deep cognitive collaboration within intelligent manufacturing environments.

为保证双足机器人的运动过程中机身的稳定性并能够抵抗一定程度的外部冲击力的干扰，设计了一种基于虚拟模型控制（VMC）与全身控制（WBC）的双足机器人力矩控制方法。该方法用虚拟模型控制对机器人模型进行简化，用得到的降阶模型求解机器人行走过程中的地面支反力；用全身控制对机器人控制任务进行优先级排序，并求解运动过程中的关节加速度；最后将二者代入刚体动力学方程中求解控制所需要的关节力矩。控制目标为驼鸟形双足机器人，使用的仿真平台为MIT机器人仿真平台。经过仿真验证，该控制方法可以有效地对目标进行控制，且机身的稳定性高，计算的求解效率高，运算复杂程度低，证明了该方法对于双足机器人控制的效果较好。

Owing to the enormous amount of work performed and harsh working conditions of agricultural and forestry machinery, many critical moving parts are subjected to long-term overloads, which results in reduced service life and hinders the further development of agricultural and forestry machinery. Therefore, reducing the wear and tear of key moving parts is of great practical importance in agroforestry production. Surface modification technology has become an important method for improving the wear resistance of key moving parts of agricultural and forestry machinery. The surface modification technology is adaptable, easy to operate, and inexpensive. Surface technologies, such as thermal spraying, built-up welding, spray welding, and laser cladding, can improve the wear resistance of key moving parts of agricultural and forestry machinery. This increases the efficiency and reduces the intensity of agricultural and forestry machinery work. Thermal spraying technology has unique characteristics such as lightweight equipment, process flexibility, easy operation, short machinery-repair time, higher adaptability, controllable coating thickness, and suitability for a wide range of base materials. Built-up welding technology offers several advantages, including high bonding strength between the fused cladding and the substrate, good impact resistance, cladding composition, and coating thickness, which can be adjusted from 2 to 30 mm. Moreover, it imparts properties that can be easily adapted to suit a variety of situations. Spray-welding technology involves preheating the alloy powder coating on the surface and subsequently heating it to 1 000–1 300 ℃. The borides and silicides produced in the coating formed good bonds with the substrate surface and between the particles. The final formation was a compact and crystalline metallic structure that formed a metallurgical bond with a substrate of approximately 0.05 to 0.1 mm. The fused coating exhibited excellent impact, abrasion, and corrosion resistance. Laser cladding is a recent technology used for manufacturing functional coatings on metal surfaces, and has high cladding efficiency, good flatness of the coating, and low overall cost. The advantages of laser cladding include the metallurgical bond between the substrate and coating, high melting efficiency, rapid cooling, high powder utilization rate, flat molten cladding, a wider selection of materials for cladding, and selective localized repair. This study outlines the working conditions and wear mechanisms of agricultural and forestry machinery. The application of four surface modification techniques—thermal spraying, spray welding, cladding, and laser cladding—to key moving parts of agricultural and forestry machinery and the advantages and disadvantages of each surface modification method are summarized. Based on this, the status of research on ground technology for agricultural and forestry machinery is presented. Finally, key scientific issues and challenges in the current research are summarized, and future research directions are discussed.

Aiming at the hysteresis nonlinearity of the piezoelectric driven systems for tunable external cavity diode lasers, a modelling and control method was proposed herein based on Rayleigh-BP model. Firstly, a Rayleigh-BP rate-dependent hysteresis model was developed by spatial expansion method, which achieved an accurate prediction of rate-dependent hysteresis nonlinearity of piezoelectric driven systems. Secondly, the inverse model of Rayleigh model was solved by an inverse algorithm, and the model was combined with a BP neural network to design a feedforward controller to compensate the systems. Finally, the feedforward control method was validated by simulation and experiments. The results show that the Rayleigh-BP model developed has high accuracy, the root mean square error is only as 0.0469 μm at 10 Hz. The feedforward control method may significantly improve the linearity of the system outputs, the root mean square error of the simulation results is as 0.0274 μm and the linear correlation coefficient R2 is as 0.999 92 at 40 Hz. The experimental results show a root mean square error of 0.0506 μm and a linear correlation coefficient R2 of 0.999 55 at 30 Hz, which greatly reduces the hysteresis phenomenon.

Hydraulic actuated robots are widely used in many scenarios due to their advantages of high payload and strong anti-interference ability. The actuators serve as the muscle of the robot to output force directly, which are significant for the performance of the robot. Among kinds of actuators, electro-hydrostatic actuators(EHA) have the advantages of high integration, high energy efficiency, and high-power density, and have been widely used in robotics, aerospace, engineering equipment, and other fields. The development status of EHA for robots is reviewed from five aspects: robot application, system configuration, hardware composition, control algorithm and development trend of EHA. Firstly, the applications of EHA in articulated robots, wearable robots, legged robots and other fields are introduced, and the system configurations are introduced in terms of fast response and energy. Then, the hardware compositions of EHA are discussed, including motors, hydraulic pumps, actuators and integrated valve blocks and other core components. In addition, this study summarizes the control algorithm of EHA for the control requirements of the hydraulic robots. Finally, the future trends of EHA for robots from the perspectives of components, drive, control and energy saving are presented.

末端执行器是采摘机器人的关键功能部件之一，在确保采摘及时性、提高劳动效率等方面发挥着重要作用，但传统的机械扭断和剪刀剪断等采摘方式使得末端执行器在采摘过程中容易对果实造成机械损伤。为了解决该问题，基于蛇嘴的生理结构和捕食机理，设计了一种新型仿生咬合式末端执行器，利用吸附机构吸附果实和咬合机构切断果柄实现果实与果柄的分离，利用柔性输送机构传输果实实现果实的无损收纳。在SolidWorks软件中设计了仿生咬合式末端执行器的三维机械结构，利用ADAMS模型仿真验证了该机构的运动合理性，并开发了分布式气动控制系统。

针对服役末期风力机叶片出现的裂纹损伤在运行过程中产生失效的危险现象，对裂纹损伤风力机叶片的应力分布规律展开研究，建立了NREL 5MW风力机叶片有限元模型，采用流固耦合分析方法研究了裂纹分布位置、极端运行阵风、不同湍流强度和风向改变对叶片裂纹应力特性的影响。结果表明：叶根处裂纹损伤最严重裂纹易向翼型中部整体开裂，叶中和叶尖处裂纹损伤易向前缘开裂；极端运行阵风下叶根处裂纹应力值波动最大可达15.2%；高湍流强度和来流风向为30°时对叶片裂纹损伤影响最大，裂纹有向中部开裂转变为前缘开裂的趋势。

针对智能工厂深度感知、智能决策和实时控制一体化应用的需求与难题，设计组态-运行模式的低耦合感知计算控制一体化体系架构，研究不同硬件平台、操作系统等底层软件平台的透明运行机制，提出面向实时与非实时的智能与控制融合方法，为感知计算控制一体化组态编程软件研制提供理论指导和技术支撑，对于智能工业控制系统的研制与应用具有重要意义，为企业数字化转型奠定基础。

The electrochemical reactions and heat generation mechanism inside lithium ion batteries (LIBs) are complex, which can be influenced by many factors. Precise mathematical modeling and visualization of LIB internal states are of importance for battery management systems. A model based on coupling electrochemical and thermal characteristic is established, in which the characteristics of battery shape and micro-geometry structure are also considered. Moreover, a method is further proposed to obtain model parameters, which combines measurements and data-driven parameter identification. To visualize lithium ion concentration in solid-phase, numerical equations describing the electrochemical reaction are established based on the lithium ion diffusion mechanism, which can explain the coupling effects of temperatures and charging rates on LIB capacity. model validation results show that the prediction error of terminal voltage is less than 50 mV and the estimation error of temperature is less than 2 ℃.

The pace of large wind turbine units was accelerating, and the reliability of core components was increasingly important for wind turbine operations. Sliding bearings had the advantages of high load capacity, long life, easy maintenance, scalability and small size, and they had advantages and great potential for the reliable replacement of wind turbine main bearings key components produced at home. The problems of main shaft rolling bearings in high-power wind turbines and the advantages of using sliding bearings on the main shaft were analyzed herein. The technical methods and application status of wind turbine main shaft sliding bearing design, materials, lubrication, and experimental verification were present in detail, and the existing problems of high-power wind turbine main shaft sliding bearings and future development trends were summarized. It is expected to provide reference for the digital design and industrial development of high-power wind turbine main bearings.

As a new type of transportation system, an evacuated tube transportation system combines maglev technology with low-pressure tube technology, which theoretically minimizes the frictional and aerodynamic resistance of trains during high-speed operation. This could potentially break the speed limits of ground rail transport, achieving speeds of 1 000 km/h or more for ultra-high-speed train. To promote the development of the evacuated tube maglev transport, this overview first summarizes the current research status of evacuated tube transport test platforms both domestically and internationally, including the U.S., South Korea, and China, where the highest model test speed has reached 1 152 km/h. It then focuses on the basic scientific issues of aerodynamics within the tube, analyzing research progress in five areas: flow states within the tube, aerodynamic loads, complex wave phenomena, aerodynamic heating, and aerodynamic noise. Finally, the outlook for the development of evacuated tube transportation is discussed, highlighting key areas for future research: refined numerical simulation methods, methods to mitigate or delay choked flow, heat dissipation pathways suitable for evacuated tube transportation system, and the evolution of flow field characteristics in the tube during prolonged train operations.

为解决城市园林绿化中人工树干涂白工作效率低下、工作繁琐、涂料浪费等问题，综合人、机、环境间的相互作用，提出并设计基于QFD与FBS的树干涂白装备。构建QFD与FBS集成模型，应用QFD方法建立树干涂白装备设计要求关系矩阵，通过FBS模型进行功能-行为-结构的映射转换；结合树干涂白操作行为分析，获得关键结构化信息，提出环绕式树干涂白装备设计方案实现对涂白方式、操作流程、涂白剂容量等的改良设计。验证表明，设计的树干涂白装备具有良好的操作性和适用性，提高了树干涂白工作的效率和质量，为城市园林绿化建设的服务系统设计提供了参考。

A pneumatic artificial muscle (PAM) actuated exoskeleton is developed for upper limb rehabilitation and augmentation. Different from rigid actuation, the developed exoskeleton combines PAM and rigid link to achieve both flexible actuation and high-precision movement. This helps to reduce the risk of unwanted injury to users during the rehabilitation process. In structural design, a combination of direct driven and cable driven is adopted to provide 3 degrees-of-freedom actuation for the shoulder and elbow joints. The compact structure helps to facilitate its wearability. This research presents the kinematics modeling of the exoskeleton, and dynamic modeling is then finished using the three-element model of PAM and Lagrange method. For the hysteresis nonlinearity of PAM, the combination of direct inverse modeling and adaptive projection algorithm is adopted to achieve adaptive hysteresis compensation without offline modeling and inversion. Finally, the feasibility of the exoskeleton and the proposed controller is verified via hysteresis compensation and anti-interference experiments. Experimental results show that the developed exoskeleton features both flexible actuation and high motion accuracy, satisfying the needs of upper limb rehabilitation and augmentation.

在故障诊断领域，由于仿真系统是近似系统，输出的仿真数据存在真实度问题。在用于诊断模型训练的目标下，仿真系统输出故障状态数据能够多大程度包含故障特征，决定仿真数据的可用性与训练模型结果的准确性。因此，提出一种仿真真实度评估方法，先分析待评估数据中包含的数据特征，再采用关键算法节点，通过总体算法方案计算，得到可信度评价指标。仿真数据真实性评估方法的研究可以帮助评估仿真模型的准确性、可用性和可靠性，同时也可以提高诊断模型输出数据的质量和精度，从而为故障诊断算法提供可靠的仿真数据结果。

针对自主研制的双体负压滚动吸附式爬壁机器人控制中存在模型参数不确定和外部干扰等问题，设计了一种径向基神经网络（RBFNN）结合改进趋近律的准滑动模态控制的自适应轨迹跟踪控制方法。在对双体负压爬壁机器人建立动力学模型的基础上，采用RBF神经网络逼近系统未知非线性动力学，消除外界未知干扰以及建模误差对系统的影响，实现了对双体负压爬壁机器人系统的精确控制。最后，将该控制方法应用于双体负压爬壁机器人进行实验验证，仿真结果和实验结果证明该方法在双体负压爬壁机器人轨迹跟踪中具有更快的收敛速度和削弱系统抖振的能力。

梳理了国内外对于微创手术机器人RCM的分类及研究，重点介绍了机械限制RCM机构的分类和实际应用情况，以及旋量理论对RCM机构研究的影响和指导作用，其中重力补偿以及主从运动控制策略均为RCM的核心技术要点。深入讨论了RCM机构性能评价体系在优化其尺寸和提高性能上的主导地位。最后，文章对未来的RCM研究方向进行了展望，包括RCM机构的多目标化和轻量化等技术研究，以及性能提升和自适应控制优化等方面，指出其在微创手术机器人和工业制造等领域中的重要贡献。

To study the compressive mechanics properties of pillar centered cubic lattice with different reinforcement directions and their filling structures, silicone rubber filled lattice structure test specimens were prepared herein. The compressive mechanics properties of two lattice structures(BCC1 or BCC2, loading direction was either the same or perpendicular to the direction of pillar rod in body centered cubic lattice with pillars) filled with silicone rubber were studied through experimental and simulation methods. The equivalent elastic modulus and compressive platform stress of two lattice structures were conducted using Timoshenko beam theory and ultimate load method. The results indicate that the proposed theoretical model may effectively predict the equivalent elastic modulus and compressive platform stress of two type lattice structures. After filling, the compression strength and energy absorption performance of the two lattice structures are enhanced, while the enhancement effect of the BCC2 structure is more significant. For the BCC1 lattice, rubber filling enhances the bearing capacity of the internal members. However, for the BCC2 lattice structure, rubber filling reduces the bending deformation of the members near the V-shaped shear band. As the radius of the lattice structure increases, the energy absorption coupling factors of both lattice structures first increase and then decrease, yet the energy absorption coupling factor of BCC1 type structure changes more significantly.

Drag reduction surfaces have been receiving increasing attention in various fields such as aviation, aerospace, and maritime due to their significant role in reducing energy consumption. And achieving high-efficiency drag reduction is significant. After hundreds of millions of years of natural selection, animals and plants in nature have developed numerous drag-reduction surfaces. The biomimetic micro/nanostructured surfaces by studying drag-reduction organisms, such as sharks, offer an innovative approach for efficient drag reduction. This review systematically summarizes the research progress of biomimetic micro/nanostructured surfaces for drag reduction and elucidates the morphological features and drag reduction mechanisms of shark-inspired surfaces and other fish-inspired surfaces. This work also describes the machining methods for biomimetic micro/nanostructured drag-reduction surfaces, including high-energy beam machining, surface etching, ultra-precision machining, 3D printing, and bio-replication technologies. Furthermore, it briefly outlines the practical applications of existing biomimetic drag-reduction surfaces in aerospace, sports events, pipeline transportation, and other areas. Finally, based on the analysis and summary of research progress, manufacturing methods, and practical applications, this study discusses the prominent advantages of biomimetic micro/nanostructured drag-reduction surfaces. It highlights the current status and challenges of machining technologies.

In Industry 4.0, the emerging technologies such as artificial intelligence, big data, and the Internet of Things are appearing endlessly, accelerating the transformation and upgrading of the manufacturing industry. In this process, industry robot plays an increasingly important role, which also lays a solid foundation for the high-quality development of intelligent/smart manufacturing. With the proposal of Industry 5.0, human centricity concept becomes popular, which has given birth to the emerging field of human-centric smart manufacturing. The boundary between human and robot in the smart manufacturing systems gets blurred, and the research on human-robot autonomous collaboration has attracted more and more attentions. Therefore, proposes a human-robot autonomous collaboration method based on large language model and machine vision to improve the intelligence level of human-robot collaboration. First, dynamic perception of the working process for human-robot collaboration is carried out by the fusion of machine vision and deep learning, where the fusion of YOLO and transfer learning is adopted to accurately identify the operate progress and the long short-term memory network and attention mechanism are combined to recognize the actions of operator. Second, the large language model is fine-tuned for human-robot collaboration to realize autonomous operating decision for smart robot during the dynamic work process. Finally, a reducer assembly case is used to verify the effectiveness of the proposed method.