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.

Sliding contact between pantograph slide plate and contact wire is the primary mean to obtain electric energy during train operation. Its current receiving quality and reliability are directly related to train operation cost and safety. The relevant research and achievements on the friction and wear properties of pantograph slides in China and abroad in recent years are summarized, and the main wear mechanism of pantograph slides is expounded. The influence of key factors, such as relative sliding velocity, contact pressure, current-carrying condition, pantograph-catenary structure parameters and operation environment on wear are analyzed. The evolutionary history of pantograph slide material is listed, and various copper-based reinforcement materials widely used at present and their friction and wear properties are introduced. The analysis results show that under the development trend of high speed and heavy load of rail transit, the wear of pantograph slide plate has become an important content that cannot be ignored. At present, experimental research, material development and application systems have been formed to a certain extent, but there are still some problems that need further research by scholars at home and abroad, such as comprehensive evaluation method of pantograph slide plate wear, wear behavior of pantograph slide plate under extreme environmental conditions, active control technology and application of wear resistant materials.

Carbon fiber reinforced plastic (CFRP) composites exhibit excellent properties such as high specific strength, high specific modulus, good design flexibility and corrosion/fatigue resistance, which endows them with wide applications in aerospace, transportation, wind power and biomedical. However, their anisotropy, non-homogeneity, low interlaminar strength and thermal-sensitive nature pose challenges in its machining. Conventional machining methods encounter problems such as severe tool wear, difficulty in controlling machining surface quality and accuracy, and the tendency to produce delamination, burrs, fiber debonding, and surface cavity. These defects significantly reduce the service performance and reliability of CFRP components. To address these challenges, non-conventional machining methods have been developed and deployed. This paper presents a comprehensive review of four widely applied special machining techniques for CFRP: laser beam machining, electrical discharge machining, water jet machining and ultrasonic vibration machining. The advantages and challenges associated with each technique are detailed, and the mechanisms of defect formation are analyzed, along with defect suppression methods. The review concludes by summarizing the suitable application scenarios for each special machining technique and offers insights into future research directions. Literature review suggests that non-conventional machining shows unique advantages in manufacturing of intricate microstructures, high-precision features and complex shapes. However, its material removal rate is significantly lower than that of traditional machining, limiting its feasibility for large-scale production. Future research should prioritize optimizing non-conventional machining process, hybrid machining process that integrate conventional and non-conventional machining, and development of advanced specialized and intelligent equipment. Additionally, the potential of other non-conventional machining techniques, such as electron beam and ion beam machining, should be actively explored. These studies are crucial for improving the precision and efficiency of advanced composite machining, fostering broader applications of high-performance material.

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.

Fatigue failure is one of the most encountered problems with cyclically loaded mechanical structures. Affected by multi-source uncertainties arising from material property, load spectrum, geometrical dimension, etc., fatigue damage evolution generally shows certain variability which cannot be ignored. In particular, the computation is sometimes very sensitive to tiny input changes, in which varying quantities over reasonable ranges can even lead to outputs with 1 000 times difference. Under this circumstance, traditional design criteria which combine deterministic models and safety/scatter factors no longer work, and methods developed from the probabilistic perspective with reasonable and accurate descriptions of uncertain inputs are highly expected to meet the requirements, including the determination of the redundancy and inspection periods, as well as the establishment of maintenance schedules, and retirement policies, in response to the tendency of reliability-based optimal design in modern structural engineering. To boost the development of probabilistic fatigue modelling and emphasize its crucial significance in fatigue reliability design, this paper systematically recalls research backgrounds, fatigue scatter sources, fatigue behaviour variability, basic elements in fatigue reliability and developing trends, and ends with conclusions.

Owing to its various distinct advantages, the abrasive waterjet (AWJ) machining technology has been increasingly used in industry. It is gaining particular favour in the machining of thermal sensitive and advanced materials where it has demonstrated as one of the most effective technologies to machine difficult-to-machine materials. Since the advent of the AWJ machining technology, a lot of research efforts have been undertaken to understand the science associated with AWJ machining, which has enabled the technology to be developed as a widely used one. To facilitate future research, this study reviews, analyzes and discusses the investigations that have been undertaken in relation to AWJ machining, covering the formation principle, flow field characteristics and impact characteristics of high-pressure waterjet and AWJ, the fluid dynamics and energy transfer on the jet impact zone, the microscopic materials removal mechanism and macroscopic kerf formation mechanism under AWJ impacts, as well as the technologies and processes that have been developed in the last decades. It provides good support to future research both theoretically and technologically. Finally, how the AWJ technology may develop and the possible research directions in the near future are proposed.

Carbon fiber-reinforced thermosetting composite (“thermoset composite”)/metal stack represents a prevalent structural format in high-end equipment, with one-shot drilling emerging as a pivotal aspect for achieving seamless connection and assembly of these stacks. However, the stark differences in their physicochemical properties often lead to frequent drilling-induced damages, posing a challenge to meeting the pressing demands of the engineering sector. Consequently, the pursuit of high-quality and efficient one-shot drilling technology for thermoset composite/metal stacks has garnered significant attention in recent years, prompting extensive and valuable research endeavors from both academic and engineering circles. This study comprehensively reviews the latest advancements in one-shot drilling technology for stacks, encapsulating four key aspects: the co-cutting removal behavior of the interface region, drilling force, heat generation, and chip behavior, low-damage drilling bits, and low-damage drilling processes. Firstly, the research progression of the co-cutting removal behavior at both macro and micro cutting levels are discussed, detailing the interface region's characteristics. Secondly, the evolution and variation patterns of thrust force, drilling temperature, and chip formation during the one-shot drilling process of stacks are analyzed. Subsequently, the historical development of low-damage one-shot drilling tools for stacks is summarized and the influence mechanisms of drilling process parameters, as well as the effects of cooling and vibration assistance, on hole quality, are elucidated. Lastly, the future trajectory and challenges facing cutting theories, drilling technologies, and process equipment for CFRP/metal stacks are discussed.

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.

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 growth of electric vehicles and the sustained development of vehicle-to-grid (V2G) technology, it is important to study and develop bidirectional charging and discharging interfaces for electric vehicles. With the advantages of convenience, flexibility, and strong interactive ability, bidirectional wireless power transfer systems have received increasing attention. The important technologies, research status, and development trends of bidirectional wireless power transfer systems for electric vehicles are reviewed. Starting from the structural composition and key technologies of the system, firstly, the related research on the main power topology based on two-stage and single-stage power conversion is summarized; secondly, the mainstream compensation topologies and their characteristics are compared, and the relationship between compensation networks and interoperability is analyzed; thirdly, the system modeling and control methods are outlined, the development sequence of efficiency optimization strategies is sorted out, and the phase synchronization technology of wireless signal transmission is summarized. Finally, the development trend and research ideas of the technology are proposed to address the limitations of existing research. It is expected to promote the technical innovation and application of bidirectional wireless power transfer systems for electric vehicles.

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 is the key basic part of many equipment and its processing technology is of great significance to improve the performance of equipment in various industries. Power skiving is an emerging gear manufacturing method, with the advantages of high efficiency, high precision and green dry cutting, has become the preferred process for complex precision gear machining on new energy and oil-fueled automotive transmission devices, robot reducers, and planetary gear transmissions. Based on the gear geometry theory of power skiving, the difference between the theory of crossed shaft gear with a pair of gear meshing and the theory of gear conjugated surface with two degrees of freedom is expounded, and the design principles and methods of several typical tools are reviewed. The influence of cutting mechanism and processing parameters on tool life and machining accuracy of gear surface is analyzed. The state of the art and existing problems of gear skiving are pointed out, and the research thoughts on gear skiving are put forward in this review.

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.

The ground will significantly affect the aerodynamic characteristics of high-speed objects near the road. The ground effect simulation technology is a crucial method to investigate the aerodynamic characteristics of aircraft in the takeoff and landing process and high-speed vehicles. It is also a main bottleneck in aerodynamic research. In order to study the aerodynamic characteristics of aircraft flying near the road or taking off and landing and the flow field distribution under the high-speed vehicle on the road surface, scholars have carried out research on the simulation mechanism of ground effect and its platform design. A review on the challenges and development directions of ground effect technology, simulation method, and simulation platform design is covered. Its core ground effect simulation technology is emphasized in the review, which are the boundary layer control method, non-contact positive-negative pressure adsorption system, and moving belt reduction under aerodynamic load, respectively. Moreover, the subsystems and design methods of the proposed ground effect simulation platform are also summarized and analyzed. In addition, the development trends and challenges of ground effect simulation technology are analyzed and prospected to provide reference and comparison for scholars in aerodynamics, vehicle engineering, and related fields.

Industrial robots are significant equipment for intelligent manufacturing, it is an essential trend for industrial robots to improve their perception and decision-making capability. As the joint stiffness of industrial robots is weak which results in poor dynamic performance, there is deviation between the actual trajectory and the desired trajectory during the working process, and it is generally necessary for the operator to use a teach pendant to debug the robot for a long time according to the actual running trajectory. In order to improve the trajectory accuracy and intelligent decision-making ability of industrial robots, this paper proposes a digital twin model construction method for industrial robots oriented to trajectory dynamic perception and autonomous decision-making, designs a digital twin framework for industrial robots with the ability of trajectory interaction between the real and the virtual, which ensures the dynamic perception and real-time mapping of the twin model on the trajectory of the physical entity by fusing the multi-origin heterogeneous in the process of the robot's operation. A method for evaluating the accuracy of industrial robot trajectory based on the trajectory error tolerance range threshold is proposed, and a polynomial function-based short-time evolution prediction strategy for the end trajectory is established by combining the robot twin model and trajectory error model, which implements an autonomous decision-making process to judge the deviation of the robot trajectory by the digital twin model when the trajectory of the physical robot is about to deviate from the desired trajectory. According to the decision result, a new trajectory is intelligently planned and the robot entity is controlled to execute. Finally, the effectiveness of the method is verified on the UR5 robot, which implements the evolutionary prediction-based autonomous decision-making control of the end trajectory, and improves the level of intelligence and robustness of the trajectory control of industrial robots.

A dynamic modeling and performance evaluation method for 3-RRS parallel mechanism is studied. The inverse displacement, velocity, acceleration and dynamic models of the mechanism are established, and the correctness of the dynamic model is verified through Solidworks motion. The mapping matrix of the driving moment and the acceleration of the mechanism in generalized coordinates is established, the translation and rotation dynamics performance evaluation index of the mechanism are proposed based on the matrix singular value theory considering the motion characteristics of one translational and two rotational of the mechanism, and the “local” dynamic performance evaluation indexes of the mechanism that can replace the global dynamic performance and improve computing efficiency are constructed. The variation of the global and “local” dynamic performance with the design parameters of the mechanism and the correlation between the two are studied, and the rationality of using the “local” performance to characterize global performance is verified.

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.

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.

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.

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.

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.

The high thrust-to-weight ratio of aero-engine imposes higher requirements on the anti-fatigue manufacturing of aero-engine compressor blades. As an important method of blade material reduction manufacturing, cutting processing directly affects the surface integrity of the blade when ensuring the geometric accuracy of the blade. Extensive studies have shown that blade surface integrity is closely related to its fatigue performance. At present, the primary blade metal materials are lightweight and high-strength titanium alloys and superalloys. Since titanium alloys and superalloys are typical thermo-mechanical sensitive materials, and there is a very complex force-heat energy field in the cutting process, the surface integrity of blades is significantly affected. Therefore, to explore the future development of anti-fatigue cutting technology of aero-engine compressor blade, this study begins by summarizing the development of blade surface and the underlying causes of fatigue failures during the cutting process. Secondly, the domestic and abroad research status of anti-fatigue cutting technology of aero-engine blades for non-bionic and bionic surface blades is investigated and sorted out. Finally, the problems existing in anti-fatigue cutting of blades are summarized, and the future development trend of anti-fatigue cutting research of aero-engine blades is prospected. This study provides a theoretical reference for the machining of aero-engine blades for anti-fatigue performance optimization.

The peristaltic pump is a typical device for the application of peristaltic transport mode,which is widely used in various industries. The peristaltic pump will produce flow pulsation in operation, resulting in unstable output flow, and to improve the pulsation of the peristaltic pump, a comprehensive study of its flow characteristics is required.The peristaltic pump problem is an inherently multi-physics field problem with strong coupling between the hose and the fluid being pumped.Using a combination of experimental and numerical simulations to study the peristaltic pump, the flow-solid interaction(FSI) model of the peristaltic pump is established, the working cycle of the peristaltic pump is simulated, and the simulation results are compared with the experimental results to verify the reliability of the numerical model.The pressure and velocity of the fluid inside the hose and the flow rate at the outlet during the working cycle of the peristaltic pump were obtained through fluid-structure interaction analysis, and the causes of the flow pulsation were analyzed and studied.It focused on the effect of the pressure block diameter, roller diameter, hose inner diameter and other factors on the peristaltic pump flow and flow pulsation.

Carbon fibre reinforced polymer (CFRP) composites are widely used in aerospace, aviation, transportation, and civil industries due to their excellent mechanical properties and lightweight characteristics. However, the layered structure and anisotropic nature of CFRP lead to defects such as delamination, burrs, and tearing during drilling, which severely affect joint quality and structural integrity. This study developed an innovative multi-scale machining modelling method that integrates macroscopic, mesoscopic, and microscopic material models into a unified finite element simulation framework. The mechanical behaviour and defect formation mechanisms of CFRP during the drilling process are systematically investigated using Abaqus/Explicit software to simulate the drilling process of CFRP laminates in detail, revealing the stress distribution and damage evolution under different fibre orientations. The simulation results show that at a 0° fibre orientation, the drilling stress is concentrated in the cutting zone, resulting in minor damage and relatively smooth surfaces. Conversely, at a 90° fibre orientation, the stress propagated along the fibres, causing severe delamination and fibre pull-out, leading to rough and irregular surfaces. These findings are validated through scanning electron microscope (SEM) observations and surface roughness measurements, with experimental results showing high consistency with the simulation data. Further analysis indicated that optimizing drilling parameters and fibre orientation could significantly reduce machining defects and improve the surface quality of CFRP holes. This study not only provides a theoretical basis for understanding the stress distribution and damage evolution in CFRP drilling but also offers practical guidance for optimizing CFRP machining processes. The application of the multi-scale modelling method allows effective prediction and control of defects during machining, enhancing the machining quality and service life of CFRP structural components.

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.

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

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

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

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

In the field of collaborative robot dynamics modeling, the presence of nonlinear friction forces at the joints results in low precision of force control based on motor feedback current. Against this backdrop, the research focuses on the nonlinear friction forces of collaborative robots to enhance the accuracy of their identification, thereby improving torque control performance. A new collaborative SCARA robot has been designed, analyzing the components of joint friction and conducting experiments on factors affecting friction, such as joint speed, temperature, and load. By collecting time series data of the robot’s position, speed, torque, ambient temperature, and load, the theoretical torque is calculated using the Newton-Euler method, the actual torque is used as the target for supervised learning to establish an Informer-LSTM-based parallel hybrid neural network model. This model identifies nonlinear composite dynamic friction forces with different time series characteristics, ultimately compensating for joint friction with feedforward torque. Experimental results show that the Informer-LSTM parallel hybrid neural network model can accurately predict long and short time series data. The data-driven comprehensive dynamic friction model is more precise at zero and very low speeds, with the average error in joint torque after friction compensation below 1%, significantly enhancing torque precision.

Edge-side fault diagnosis requires lightweight deep models. They are, typically, empirically handcrafted by experts, which is time-consuming and labor-intensive. The configurable resource capacity for the edge hardware is not considered in manual lightweight models; therefore, they may not meet deployment requirements. Here, a method based on a low-pass screening neural architecture search is proposed. Fault-diagnosis models are automatically designed for edge hardware considering the hardware configurable resource capacity. First, an empirically inspired search space is designed to reduce the search difficulty in lightweight models. Meanwhile, a low-pass screening reward function is modeled to guide an agent iteratively screening lightweight diagnostic models meeting the hardware configurable resource capacity condition during the search process. Finally, Pareto-optimal domination is used to obtain a competitive Pareto-optimal solution set, providing an optimal model for the edge hardware to achieve fault diagnosis of wind-turbine gearboxes. The feasibility and effectiveness of the method were verified on a gearbox’s test from a drivetrain diagnostics simulator and on measured wind-farm case. The results indicate that the searched models are superior to the competing models in terms of accuracy, FLOPs, and parameters. Particularly, in the application case, LSNAS-Netb achieved 3.06% and 3.65% higher accuracies compared with the deep model GoogLeNet-v1 and edge-side-friendly MobileNet-v2, respectively, with 15.56×and 6.19×fewer parameters and 5.47×and 1.18×fewer FLOPs, respectively.

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.

Traditional magnetic field sensors are susceptible to environmental electromagnetic interference, which is difficult to be applied to magnetic nondestructive testing under complex working conditions. Hence, a magnetic field sensor based on Terfenol-D and fiber grating is developed, and a sensor sensitivity enhancement method based on static bias magnetic field tuning is proposed. Firstly, based on the magnetostriction and fiber grating sensing theory, a Terfenol-D based fiber grating magnetic field sensor model is established, and the relationship between the wavelength shift of the fiber grating and the applied magnetic field strength of the Terfenol-D material is analyzed. Secondly, a static bias magnetic field is introduced to pre-magnetize the Terfenol-D material to adjust the detection sensitivity of the sensor to magnetic field changes. The theoretical analysis and experimental test results show that the magnetic field detection sensitivity of the sensor can be effectively improved to 9.38 pm/mT by the tuning of the static bias magnetic field, which is about 8.55 times compared with that of the unoptimized one. Finally, the optimized fiber grating magnetic field sensor based on Terfenol-D is applied to the magnetic leakage detection of steel plate, which not only realizes the defect localization and the quantitative characterization of the defect depth, but also shows better antimagnetic interference ability than that of the traditional Hall sensor.

Composite materials composed of materials with different tensile and compressive properties are widely used in practical engineering, however, there are few studies on the topology optimization design of tensile/compressive structures composed of such materials, and in-depth research is urgently needed. A topology optimization method for tension/compression structures based on a four phase material model consisting of pure tension, pure compression, orthotropic materials, and void property materials. is proposed. The method adopts a hybrid stress element discrete design domain and establishes a criterion for determining the tension/compression state of elements based on principal stress values and tension/compression tolerances. This can more accurately identify the tension/compression state of elements and achieve material distribution based on element stress states. For the design of multiple load cases, a method for determining the element tensile/compressive state under multiple load cases based on the tolerance limit of tensile/compressive strain energy difference is constructed. The use of four phase materials models for tensile/compressive structural design can achieve better performance design results. The effectiveness of proposed method is verified through several numerical examples, as well as the influence of various parameters, boundary conditions, and multiple load conditions on the design results.

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.

Research on self-tuning variable stiffness torsional vibration suppression technology is conducted to address multi-modal torsional vibration in vehicle powertrain systems under wideband excitation. The working principle of the proposed parallel variable stiffness frequency-tuned compound torsional damper is introduced, the dynamic model of the torsional vibration system with the damper is established, the transient changes of the inherent vibration characteristics of the torsional vibration system during frequency tuning is analyzed, the optimal frequency tuning control scheme for the parallel variable stiffness frequency-tuned compound torsional damper absorber block that is suitable for system operating conditions is proposed, a torsional damper frequency tuning validation test bench is constructed, semi-active control experiments on the torsional damper are conducted, and its frequency tracking characteristics with respect to external excitation frequencies are tested. The research results show that the self-tuning variable stiffness absorber block can effectively improve the vibration reduction performance of the torsional vibration system, and the improved frequency tuning scheme can significantly reduce the vibration response of the torsional vibration system. Moreover, the semi-active torsional damper performs significantly better than the passive damper throughout the frequency tuning range, which verified the effectiveness and reliability of the proposed frequency tuning improvement scheme.

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.

Material kitting is an important guarantee and prerequisite for the efficient and orderly conduct of the assembly process of complex products. With the continuous improvement of digital and network capabilities in assembly workshops, the traditional material kitting mode in assembly processes is undergoing a shift from product-level kitting to process-level kitting. This shift poses higher demands for the accuracy of material demand forecasting. Due to the extensive manual operations involved in the assembly process, and the frequent occurrence of mixed-flow co-linearity between developmental models and batch production models, traditional simulation methods face challenges in predicting process completion times, consequently impacting material demand forecasting. To better support production scheduling in the assembly process and ensure the orderly execution of complex assembly tasks involving multiple varieties and small batches, a method for predicting the process-level kitting time of assembly materials for a specific type of anti-aircraft missile complex product based on digital twins is proposed. This involves constructing a digital twin model tailored to the assembly process of complex products, providing a simulation environment for the time consumption of tasks in each stage of the assembly kitting process. Based on the simulation results data of the assembly kitting process and combined with real-time production data of assembly tasks, design a material process kitting time prediction algorithm model. By capturing the relationship between workshop status and manual efficiency through historical production feature data, enhance the accuracy of predicting the kitting time for pending assembly tasks. Finally, an application demonstration is conducted in a specific anti-aircraft missile assembly workshop to validate the effectiveness of the proposed method.

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.