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

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

With the rapid development of digital manufacturing technology, a large number of machining process instances have accumulated in enterprise databases. Based on the basic principle that “geometric similarity likely leads to process similarity”, effective reuse of process knowledge can be achieved through identifying and extracting similar three-dimensional geometric process information, thereby enhancing the intelligence level of process decision-making systems and significantly shortening product development cycles. Against the background of rapid development in NC machining process reuse technology, systematically grasping its current status and future trends and providing comprehensive literature reviews for process designers has important theoretical and practical significance. The research systematically analyzes and summarizes the latest research progress of NC machining process reuse technology from three dimensions: first, at the macro process reuse level, methods for reusing the overall processing route of products are discussed; second, at the micro process reuse level, focus is placed on the precise extraction and application technology of process knowledge in specific processing links; finally, process reuse technology based on machine learning concentrates on the processing of unstructured CAD model data and the complex mapping relationship between them and process information. These research results not only have important theoretical guiding value for improving process design efficiency, but also show significant application prospects in promoting the improvement and optimization of enterprise process knowledge management systems.

The processing channels of integral components such as blisk, integral impeller, and closed impeller of aero-engine are twisted, narrow, and deep, and are prone to various machining interference, which are typical difficult-to-machine components. To improve their machined surface quality, the robotic belt grinding process that is widely used as the last finishing process of the integral component is taken as the research object, and a multi-objective planning method of interference-free machining postures for robotic belt grinding of integral components is presented. With this method, a definition of interference-free robot configuration space (IFRC-Space) is first proposed. Next, an exploration experiment on the evolution law of IFRC-Space along the toolpath when grinding integral components is carried out, which concluded that IFRC-Space varies continuously along the toolpath. Based on the conclusion, a rapid computation method of IFRC-Space is proposed by using the edge detection operator of images. Then, a multi-objective optimization algorithm of grinding postures is advanced based on IFRC-Space. With the algorithm, the indicators including interference avoidance, singularity avoidance, smoothness of grinding postures, and the kinematic performance of the robot can be comprehensively considered, which enables the automatic generation and multi-objective optimization of the interference-free machining postures for robotic belt grinding of integral components. The effectiveness and practicability of the proposed method are verified by toolpath planning experiments for robotic belt grinding of aero-engine blisk and closed impeller.

The aircraft skin is the primary component forming the aerodynamic shape of an aircraft, characterized by large size, thin wall (thickness 2～6 mm) and complex structure. Currently, manufacturers generally adopt a manual comparison-marking-trimming method to remove the edge allowance of the skin parts, leading to large cumulative human errors and difficulties in controlling assembly quality. Vision/force-guided industrial robot milling with high-flexibility and large operation range provides a novel approach to solving these problems. However, difficulties in simultaneous calibration of dual-robot systems, smooth path generation for machining and accurate control of the robot’s trajectory have become the bottlenecks restricting the application of robot milling for the aircraft skin. The above challenges can be summarized as the simultaneous decoupling of spatial transformation and the quantitative control of pose errors. To address these issues, this paper conducts in-depth research on dual-robot system calibration, smoothing machining path generation and closed-loop feedback control of the robot’s end-effector. The Part I proposes simultaneous calibration method of dual-robot system for robotic tracking/measuring-machining, establishes kinematics model of robot-tracking system and studies method to generate a smooth machining path for aircraft skin. The Part II studies closed-loop feedback control model for robot’s end-effector under external tracking system, develops closed-loop feedback control system for robot. The simultaneous calibration accuracy test of dual-robot, the trajectory accuracy test of end pose with closed-loop control, and the robotics milling accuracy test of typical skin samples are carried out to validate the effectiveness of the proposed methods.

The new energy vehicles, exemplified by electric vehicles（EVs）, have embraced unprecedented opportunities for development. Lithium-ion batteries（LIBs）, leveraging their advantages such as high energy density, low self-discharge rate, and long lifespan, have emerged as the mainstream choice for EV power systems. However, the significant degradation of LIBs performance at low temperatures directly leads to reduced driving range, prolonged charging time, and potential safety hazards for EVs, posing a core challenge to their widespread adoption. Low-temperature heating, as one of the key methods to overcome the performance bottleneck of batteries at low temperatures, is currently the focus of industry research. This study comprehensively summarizes and discusses the latest advancements in low-temperature heating technologies for LIBs and their current application status in real vehicles, encompassing external heating, internal heating, and hybrid heating. It thoroughly elaborates on the principles, latest progress, strengths, weaknesses, and potential optimization opportunities of each technology. Additionally, it conducts a qualitative comparison of each technology and analyzes the current application status of heating technologies in real vehicles. Finally, the paper explores the future development prospects of low-temperature heating technologies, with an emphasis on key technological breakthroughs and opportunities, providing a comprehensive perspective for the next steps in research and real-vehicle applications of low-temperature heating technologies.

Blade vibration and tip clearance are critical parameters reflecting the operational status of aero-engines, containing abundant fault and health information. Real-time monitoring and deep analysis enable fault diagnosis and early warning of engines. This article reviews contact-based measurement methods for blade vibration as well as non-contact measurement techniques such as blade tip timing and tip clearance monitoring. It summarizes important research achievements in related technologies domestically and internationally in recent years, focusing on three main aspects: Types of blade vibration and typical faults, monitoring and identification methods, and fault diagnosis and warning methodes. In particular, the application of these techniques in typical faults such as flutter, surge, and rubbing is emphasized. Finally, the future development trends of aero-engine fault diagnosis and warning technology based on tip monitoring are prospected from five perspectives: high-precision high-speed acquisition, mechanism and evolution path analysis, multi-source fusion testing, fault database optimization, and machine learning-enabled intelligent diagnosis.

In-space additive manufacturing (ISAM) is considered a key technology for achieving deep space exploration, efficient utilization of space resources and long-term habitation on extraterrestrial bodies. When compared to conventional terrestrial additive manufacturing, the development of ISAM technology is uniquely challenged by the space environment, which is characterized by high vacuum, intense radiation, microgravity and extreme temperature variations. Breakthroughs are required in equipment compatibility, process control, raw material selection and other aspects. ISAM research is currently focused on two cutting-edge fields: On-orbit manufacturing and surface construction on extraterrestrial bodies. Various types of forming processes, including materialextrusion, directed energy deposition, powder bed fusion, stereolithography and computational axial lithography, are being developed. The current research status and development trends of ISAM technology are reviewed, with comparisons made between the process principles and characteristics of various ISAM technologies. Typical microgravity verification platforms and their testing methods are summarized. Cases of ISAM technology research and application in actual space environments are outlined. The significant challenges and potential opportunities faced in the development of ISAM technology are analyzed and key directions for future research are anticipated.

Multistable characteristics represent a unique nonlinear mechanical phenomenon. When a system exhibits multistable characteristic, it means that there are multiple stable equilibrium states (states where the energy reaches a local minimum). In these states, even if the system is subjected to small external disturbances, it can maintain its state by itself without energy input. Multistability can exhibit a variety of mechanical characteristics/behaviors, such as negative stiffness, self-balancing, and disturbance resistance, etc. It has gradually shown great application value in fields such as aerospace, smart robot, and biomedicine, and has attracted a large number of researchers in recent years. This research reviews the typical research achievements on multistable mechanisms and structures in recent years. First, the principles, configurations, and design methods of different multistable mechanisms/structures are summarized from the aspects of compliant mechanisms, origami structures, kirigami structures, soft materials, and tensegrity structures; Then, the typical applications of multistable mechanisms/structures in various application scenarios are introduced; Finally, it offers insights into future development trends, aiming to provide new perspectives for enhancing the design and application of multistable mechanisms and structures.

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

Molten salt possesses excellent thermophysical properties such as high heat capacity, thermal conductivity, and thermal stability, making it widely used as a heat storage medium in fields such as nuclear power and concentrated solar power generation. However, high-temperature molten salt exhibits severe corrosiveness towards metallic materials. Based on the evaluation and mechanism of molten salt corrosion of stainless steel, the main strategies to slow down the corrosion of molten salt on stainless steel are introduced from the aspects of molten salt modification and surface modification of materials. Firstly, the paper discusses the evaluation of molten salt modification methods such as thermal purification, chemical purification, and addition of nanoparticles to reduce metal corrosion. Subsequently, it analyzes the research progress on material protection methods, including aluminum alloy coatings, surface fractal textures, graphitization coatings, nanoparticle coatings, and aluminum-containing stainless steel pre-oxidation, and their effects on mitigating molten salt corrosion. Finally, it emphasizes the importance of developing aluminum-containing stainless steels for the safety design, manufacturing, and operation and maintenance of concentrated solar power systems.

To address key challenges in aviation intelligent manufacturing, such as low data-knowledge collaboration efficiency, difficulties in tracing assembly deviation sources, lagging process parameter optimization, and insufficient virtual-real interactive verification, this study proposes an AI twin control methodology framework with industrial large models as the cognitive engine, and constructs a digital twin closed-loop control framework covering the entire "perception-diagnosis-decision-verification" process. Through networked associative modeling of knowledge graphs, semantic fusion and dynamic reasoning of multi-source heterogeneous data are realized, an industrial large model corpus for aviation manufacturing is established, and a professional knowledge base with autonomous evolution capabilities is formed. An industrial large model algorithm library for multi-scenario intelligent decision closed-loops is developed: Bayesian causal inference is used to analyze multi-level coupled causes of assembly deviations; incremental ensemble learning is integrated to achieve dynamic evolution prediction of multi-source coupled deviations; and transfer reinforcement learning is applied to break through the bottleneck of cross-scenario parameter optimization. Finally, a virtual-real bidirectional driven verification closed-loop is built using digital twin technology. Verification results based on the fuselage panel assembly of a certain type of civil aircraft show that the proposed method significantly improves the automatic assembly accuracy of stringers, with the one-time assembly and adjustment success rate of stringers increased by 24% compared with traditional methods. It also enables real-time inspection of drilling and riveting quality, achieving an accuracy rate of 98% in identifying continuous drilling and riveting defects. By constructing and evolving a domain-specific knowledge base, this study deeply drives the full-process closed-loop from deviation causal tracing to twin verification, and realizes a paradigm shift in manufacturing decision-making from experience-driven to model cognition-driven.

In order to optimize the electromagnetic characteristics and viscous friction loss of wet permanent magnet brushless DC motor(WPMBLDC) at high speed, an 8-pole 12-slot WPMBLDC was taken as the research object, and the finite element method was used to study the electromagnetic performance and internal flow field characteristics of the motor. In order to reduce the peak value of no-load cogging torque, output torque ripple and viscous friction loss, and improve the average output torque, the no-load back electromotive force of the motor should not exceed 250 V. The evolutionary algorithm is used to analyze the sensitivity and influence of stator slot structure parameters, armature diameter, air gap width, pole arc coefficient and permanent magnet thickness on the electromagnetic characteristics and viscous friction loss of the wet motor, and the optimization and experimental verification are carried out. The results show that the air gap width, slot opening and pole arc coefficient are the significant factors affecting the electromagnetic characteristics of the wet motor. The groove opening and the outer diameter of the rotor are the significant factors affecting the viscous friction loss of the wet motor. After optimization, the peak value of cogging torque is reduced by 48.23%, the average torque is increased by 53.5%, the torque fluctuation is reduced by 16.11%, and the viscous friction loss is reduced by 14.8%. The optimization effect is more significant.

During the mission of the launch vehicle, the separation mechanism, as a key component, is responsible for effectively detaching various stages of the rocket structure, with its reliability directly influencing the mission success rate. In recent years, as reusability has become a fundamental requirement in rocket design, non-pyrotechnic separation mechanisms have attracted increasing attention for their high reliability and safety, creating an urgent need for comprehensive reliability analysis to fully leverage their advantages in repeated applications. The definition, types, and working principles of separation mechanisms are detailed, and a systematic review of existing methods and the significance of mechanism and structural reliability research is presented. Existing failure mode identification approaches and reliability analysis methods are examined, highlighting their limitations when applied to reusable non-pyrotechnic separation mechanisms. The future development trends of reliability research on reusable non-pyrotechnic separation mechanisms are also discussed, aiming to provide a reference for theoretical research and technological innovation in the system reliability analysis of these mechanisms.

Blade failure is one of the most serious failures of the gas turbine. Real-time monitoring and early warning of blade failure is an effective method to improve the operational reliability. Based on the casing vibration signals, this study explores the influence of blade conditions on the pressure and vibration of the casing of gas turbine, in order to solve the difficult problem of early warning and diagnosis of blade failures. The dynamic pressure in the wake flow caused by the normal and fault conditions of the rotating blades is firstly simulated based on the Fourier series superposition, and the transmission characteristics of the dynamic pressure in the casing are analyzed. Secondly, a simplified finite element model of the casing-stator-blade based on plane beam element is proposed and established, and the natural frequency and mode shapes of the casing are solved, and the vibration characteristics and response law of the casing under normal and fault wake pressure are analyzed at the same time. Based on the above theoretical, the blade early warning indicators and diagnostic process applicable to the failure of gas turbine are proposed. Finally, it is verified by two actual industrial blade failure cases of gas turbines. The results show that the wake pressure generated by the rotating blade mainly consists of multi-order high-frequency blade passing frequency components and rotational frequency components, and the amplitude of each order decreases with the increase of the order; blade failure causes the change of blade passing frequency amplitude in the casing vibration signal, and the more significant is the sidebands on both sides of the frequency with the rotational frequency as the interval, and these features are crucial for the identification of blade failure, which is the basis for the establishment of blade fault warningindicators. The results provide theoretical guidance for improving the operation reliability of the gas turbine.

With the continuous expansion of new energy power generation, the demand for energy storage is significantly increasing. Electrochemical energy storage technology, represented by lithium-ion batteries, has become the dominant technology in this field due to its high energy density and long cycle life. However, safety issues in large-scale lithium-ion battery energy storage power stations have become increasingly prominent in recent years, with frequent thermal runaway and fire accidents. The internal temperature of a lithium-ion battery is regarded as a key indicator of its operational status, providing precise early warning for thermal runaway. Currently, the Battery Management System(BMS) primarily monitors temperature using sensors placed on the battery surface, but this method is associated with measurement errors and response delays. First, the microstructural changes and thermal runaway mechanisms during internal temperature rise in lithium-ion batteries are introduced. Then, an analysis and review are conducted from three aspects: internal temperature measurement using embedded sensors, internal temperature estimation based on Electrochemical Impedance Spectroscopy(EIS), and internal temperature prediction using machine learning algorithms. By comparing the advantages and disadvantages of these three methods, research progress is systematically summarized, and future development directions are prospected. The research results provide a theoretical reference for the accurate prediction of lithium-ion battery temperature in BMS and offers new ideas and a foundation for research on thermal runaway early warning.

Intelligent fault diagnosis method based on data-driven is the research focus of modern mechanical system. However，due to practical limitations，it is impossible to obtain samples of all working conditions or fault types，which makes the data-driven model lack of specific training data，leading to unsatisfactory performance of the method. In view of the above challenges，a rolling bearing fault diagnosis model based on contrastive embedding feature generation is proposed. By learning the faults with sufficient samples, the common features of existing faults and missing faults are mined to realize the generation and diagnosis of specific types of missing faults. Firstly, the spectrogram of the corresponding fault characteristics is obtained by analyzing the time-frequency characteristics of the original vibration signal; Secondly, the feature extraction network which has been pre-trained and fine tuned is used to extract the fault features in the spectrogram; Then, the extracted features are input into the confrontation network based oncontrastive embedding feature generation and Wasserstein generative adversarial network with gradient penalty. Through the cross stage contrastive embedding method, and according to the pre-defined fine-grained fault description, the feature generation of missing faults is completed，and the actual and generated fault features are mapped into the embedding space; Finally, the classification of all fault types including known and unknown is completed in the embedded space. The proposed method is applied to three typical zero-shot fault diagnosis scenarios. The experimental results show that compared with other methods, the proposed method can diagnose the known and unknown fault types, has advantages in accuracy.

Intelligent machining, as the core direction of manufacturing industry's transformation to high precision and autonomy, is highly dependent on data enablement technology for in-depth analysis of heterogeneous data from multiple sources and intelligent decision support. In this study, we systematically sort out the research progress of data enablement technology in the field of intelligent achining, put forward a full-process enabling framework covering “On-machine measurement-Signal pre-processingFeature extraction-Multi-source fusion-Data governance”, and analyze three key technologies, namely, multi-source sensing information fusion cloud-fog-edge collaborative computation, and dynamic migration of process knowledge. The study shows that the data enabling technology is effective and efficient. The study shows that data enablement technology effectively enhances the real-time response and robustness of the process, significantly strengthens the guarantee of process control and quality consistency, and promotes the leap of intelligence level in multiple industrial scenarios. However, bottlenecks such as the difficulty of modeling multi-physical field coupling in complex process scenarios, the lack of fidelity of virtual-reality mapping, and the low efficiency of knowledge migration from small samples still restrict the application of the technology. Aiming at the above challenges, this paper proposes a future technology path that integrates dynamic federated learning and causal reasoning to provide a systematic research perspective for the manufacturing industry to evolve to a higher-order stage of cognitization.

To address the challenge of balancing real-time performance and adaptability in hybrid electric vehicle (HEV) energy management, this paper proposes a real-time hierarchical energy management strategy (EMS) that integrates deep reinforcement learning (DRL) with model predictive control (MPC). At the upper layer, a deep Q-network (DQN) is employed to construct an EMS controller that rapidly plans a reference trajectory for the state of charge (SOC) prior to vehicle departure. At the lower level, a Long Short-Term Memory (LSTM) network is first employed to construct a velocity predictor, forecasting the velocity sequence over a future time domain. Subsequently, an MPC controller is designed to achieve optimal power flow allocation by tracking the SOC reference trajectory. The proposed strategy is then comprehensively compared with dynamic programming (DP) and rule-based strategies across different test conditions. Simulation results demonstrate that the proposed strategy achieves over 90% of the fuel economy attained by the DP strategy while exhibiting strong real-time application potential. Finally, hardware-in-the-loop (HIL) experiments validate the practical applicability of the proposed strategy.

An artificial intelligence architecture—the chain-of-thought (CoT) paradigm text-based multimodal intelligent agent—for operation and maintenance (O&M) of mechanical equipment is proposed. Firstly, to address the challenge of constructing high-quality, large-scale monitoring data-to-fault mode mapping datasets in real-world engineering applications, a chain-of-thought dataset construction strategy integrating monitoring signals, mathematical features, text descriptions, and fault mode is proposed. Based on this, a signal-to-text (Sig2Txt) model driven by a signal-text data generator is developed. Subsequently, a high-quality specialized textual dataset for O&M in the mechanical equipment domain is created, and an intelligent O&M-specialized large language model is established through instruction fine-tuning on a general large language model. Finally, by organically integrating the above models based on large model intelligent agent technology and guided by the operational thinking patterns of human experts in equipment maintenance, a chain-of-thought paradigm text-based multimodal intelligent agent for intelligent O&M is formed. Testing results indicate that this model can achieve chain-of-thought parsing and mapping from multimodal input decision-making, with an accuracy exceeding 70% on ISO Category III vibration analyst test questions, thus reaching expert-level performance. In evaluations with engineering cases and publicly available multimodal datasets, the proposed model outperforms existing mainstream large models. More importantly, owing to the proposed low-cost, high-quality multimodal CoT large-scale dataset construction framework, and the unique advantages of a “text-based” approach in terms of encompassing knowledge, high-level abstraction, and interpretability, the model shows considerable scalability and development potential.

The in-situ indentation testing inside the scanning electron microscope(SEM) is an effective technology for investigating and revealing the deformation damage process and mechanism of materials under contact loading. However, this technology is basically monopolized by foreign countries. Accordingly, an in-situ micro/nano indentation testing device inside the SEM with independent intellectual property rights is designed and developed, and its size is 130 mm×56 mm×53 mm. The structure, electromechanical system and testing processes of this device are described in detail, along with the calibration and performance testing of its core components. Corresponding to the load range of 500 mN and displacement range of 19 μm, the load noise and displacement noise are less than 0.03 mN and 2 nm respectively, which meets the performance requirements for micro/nano indentation testing. Using the reference mapping method, the frame compliance of the developed device is calibrated. The load-depth curve obtained by the device after calibration basically coincides with that obtained by the commercial indentation instrument. Finally, the in-situ indentation tests of the Zr-based metallic glass are performed inside the SEM by using the cubic corner indenter. The effects of maximum indentation load and loading rate on the serrated flow behavior and the characteristics of indentation-induced shear bands of metallic glasses are investigated, verifying the practical application potential of this device for performing micro/nano indentation testing inside the SEM.

The overall accuracy of circular-grating angle encoders is known to determine the performance of high-end equipment and precision metrology. To address the sparse sampling and incomplete full-circle characterization inherent to discrete calibration with an autocollimator and polygon prism, a self-calibration framework without external references is proposed. An angle-error detection model based on Fourier time-shift characteristics is formulated; an analytical transfer relation between multi-sensor angle measurements and the error spectrum is derived; and three data-fusion weighting strategies—minimum-variance, arithmetic-mean, and magnitude-based—are designed so that continuous full-circle error extraction and compensation can be achieved. Simulations (including Monte Carlo analyses of installation phase and noise) and experiments are performed with respect to sensor layout, fusion weights, sensor consistency, and sampling density. An angular-metrology platform is constructed using a Renishaw REXM20USA255 disk with T2001-30A reading heads and a Ti2000A12E interpolator, and independent comparisons are conducted with a photoelectric autocollimator and polygon prism. It is demonstrated that minimum-variance weighting yields the best detection performance; after self-calibration, an absolute full-circle error of ±0.8″ is achieved with a single sensor. By increasing sampling density, residual harmonic errors are markedly reduced (PV decreases from 0.29″ to 0.10″). Considering observability and noise robustness, a three-reading heads layout is recommended. The effectiveness of the self-calibration approach is thereby validated, and a deployable solution is provided for scenarios in which external high-precision instruments cannot be used.

Optimizing additive friction stir deposition manufacturing processes has been an urgent need. In order to overcome the problem of uneven distributed mechanical performance, a study of additive friction stir deposition manufacturing process for 6061 aluminum alloy has been conducted. The effects of traveling speed and shoulder height on the macroscopic morphology, microstructure, and mechanical properties of the deposited aluminum alloy are analyzed. A physical model of material filling during the deposition process is also established. The results show that internal weak bonding in deposition layer is the main cause of the decrease in tensile performance along the deposition direction. When weak bonding occurred, the arc-shaped pattern on the surface of the deposition layer became shallower or disappeared in local areas. The formation of weak bonding can be suppressed by setting the actual feeding rate greater than the theoretical feeding rate required to just fill the preset gap in a single rotation cycle of the shoulder, which improve the metallurgical bonding quality and mechanical properties. The proposed calculation methods for material fill volume and feed rate based on the deposition process of plasticized materials provide a theoretical basis for a deeper understanding of the friction stir additive process and for improving deposition quality.

Advanced materials are new materials with excellent properties, which are characterized by high strength, extreme temperature resistance, high biocompatibility, or special functions such as self-healing, shape memory and environmental response. Advanced materials have become the key foundation to support the development of high-end manufacturing. However, due to the high hardness, high melting point, high brittleness and other characteristics of advanced materials, the disadvantages of traditional processing methods are gradually revealed. The additive manufacturing technology based on material layer by layer cumulative forming is especially suitable for advanced materials to form complex three-dimensional structures, and has made significant progress in product development and industrial application. Therefore, a review of the research and development in additive manufacturing of advanced materials is provided. Firstly, the related concepts of advanced materials and additive manufacturing are elaborated, and the development prospect of their synergy is analyzed; Secondly, based on different forming principles, the development status and technical advantages of various advanced material additive manufacturing are introduced; Thirdly, the advanced material system is systematically reviewed, and its application status in additive manufacturing is summarized; Then the application status of the technology in different fields is shown, and the actual cases are listed; Finally, the existing problems of this technology are discussed, and the future development direction is prospected.

In Part I, the methods of kinematics modeling and system parameter identification of robot-tracking system are studied, simultaneous calibration model of dual-robot tracking/measure-machining integrated system is established, and a method of smoothing machining trajectory generation of skin parts is proposed. The robotic tracking/measure-machining system has a good initial geometric accuracy, and the executable robot milling trajectory is generated. However, when the machining robot moves in a wide range, it is affected by several geometric and non-geometric errors such as kinematic parameter error, joint angle error, weak stiffness deformation, joint friction error and cutting vibration error. Generally, the trajectory accuracy is only in the millimeter level, and the robotic machining system without feedback compensation is difficult to accurately process the skin contour. Therefore, Part II further establishes the closed-loop feedback control model of the robot’s end pose under the high-precision external tracking system, develops the corresponding hardware and software system, and achieves the real-time tracking and feedback compensation of the robot's end pose based on the ±10μm measuring accuracy originating from the external tracking system. Finally, the simultaneous calibration accuracy test of dual-robot, the trajectory accuracy test of end pose with closed-loop control, and the robotics machining accuracy test of skin samples are practically verified.

The propagation of thermal fatigue crack is the main reason of railway brake disc damage and even failure. In order to study the crack opening and closing state, contact state of crack surface and crack propagation behavior, the virtual crack closure technique and finite element secondary development are combined to analyze the finite element model of the brake disc with prefabricated cracks. The results show that the presence of surface crack of the brake disc has no significant effect on the compressive stress field during braking, but it affects the distribution of residual tensile stress field, and the stress concentration phenomenon appears at the crack front. During the braking process, the normal contact pressure between the crack surfaces of the brake disc changes layer by layer along the crack depth direction from the friction surface, and the relative magnitude of the normal contact pressure between the surface and the deep changes. The normal relative displacement between the crack surfaces of the brake disc diffuses from the crack center to the crack front in a shell shape, and gradually increases to the maximum with the cooling process. With the crack propagation, the crack opening time of the brake disc gradually delays and the crack appearance tends to flatten.

Aiming at the lateral sway of the tail car when a domestic type of urban EMU with a speed of 160 km/h is running in a single-track tunnel, the influence mechanism of aerodynamic excitation on the lateral sway of tail car was revealed, and the mitigation measures are studied from the perspective of suspension parameters optimization. According to the actual shape and dynamic parameters of the city EMUs, the aerodynamic and multi-body dynamic models of the 8-car train are established respectively. Overset mesh technique is used to obtain the aerodynamic forces of train, which are applied to the vehicle body as external excitations, and then the fluid-solid coupling simulation model of the train is constructed. And the root locus analysis method is used to grasp the evolution of vehicle suspension modals and hunting modals, the results show that in the single-track tunnel, the aerodynamic forces on each car from front to rear gradually increase, and the difference between the head car and the tail car is very obvious. The aerodynamic forces of the tail car are in the form of harmonics, and the main frequency of each aerodynamic force is in the range of 0.5-3 Hz, which is close to the vibration modals of the vehicle system itself. The upper rolling modal and yawing modal of the vehicle have modal-hopping and coupled resonance with in-phase and out-phase of hunting motion of two bogies respectively, which causes primary serpentine of train. The main frequency of the large yaw aerodynamic moment have the coupling resonance with the hunting modal frequency of the whole vehicle，which is the important reason for the lateral sway of the tail car. Changing the hunting frequency or increasing the damping ratio by optimizing the suspension parameters of vehicle is an effective measure to alleviate the sway of the tail car. Reducing the damping coefficient of the anti-yaw damper, increasing the damping coefficient of the secondary lateral damper, reducing the longitudinal stiffness of the primary, increasing the equivalent conicity, and adding the lateral damper between front and rear cars can relieve the lateral sway of the tail car to a certain extent, and improve the running stability and ride comfort.

The design and application of modular robots represent a crucial approach to addressing future space multitasking challenges. The morphology of fundamental modular units directly influences the task adaptability of modular robots. To tackle the nonlinear mapping problem between space multitask requirements and modular unit morphology, this study proposes a morphology design and performance analysis method for self-reconfigurable modular robots tailored for space multitasking. First, through graph theory-based inverse mapping and the formulation of mapping rules, two types of basic modular unit morphologies are constructed. Body degrees of freedom (B-DOF) are introduced to enhance posture adjustment capability between interfaces, and morphological parameters are characterized. Next, an improved adaptive differential evolution algorithm is employed, with the unit shape and B-DOF angle as optimization variables. A performance evaluation index encompassing stress distribution uniformity, motion flexibility, and workspace is established to optimize and assess the morphology, yielding optimal parameters: a spherical exterior and a B-DOF angle of 45°. Subsequently, the performance expressions of the two modular units are analyzed, including workspace, motion flexibility, isotropy, and shell stress-strain distribution across different motion transmission chains, demonstrating the superiority of the proposed morphological design. Finally, a simulation of the entire truss assembly process is conducted as a multitask case study. The results show that the modular robot, composed of these units, can autonomously reconfigure into single-arm, dual-arm, and multi-branch configurations, successively accomplishing tasks such as truss grasping, transportation, and assembly. The theoretical findings provide valuable insights for the design of modular robots.

To overcome the limitations of a single non-destructive testing method, a sensing structure that integrates magnetic flux leakage（MFL） detection and electromagnetic acoustic transducer（EMAT） detection methods is proposed to simultaneously detect surface and internal defects in steel specimens. The proposed hybrid sensor structure makes full use of the magnetic circuit characteristics of the MFL and EMAT. The combination of the yoke and permanent magnets creates a horizontal magnetic field inside the sample to detect defects on and near the surface of the sample. Secondly, the local vertical magnetic field provided by the magnetization structure of the MFL interacts with the EMAT excitation coil to produce ultrasonic waves in the specimen to identify internal flaws in the specimen. The excitation source for MFL is quasi-static and the ultrasonic signal is a pulsed excitation source at 2 MHz. The significant frequency difference between the two signals is helpful in reducing mutual interference. Simulations and experiments show that the proposed sensing structure can overcome the blind zone caused by near-field emission in EMAT and the depth limitation of MFL, and is able to simultaneously detect and classify both surface defects and internal blind hole defects in large thickness ferromagnetic sample.

Wide hot-rolled strip is a core raw material for modern manufacturing，and its efficient large-scale production combined with refined quality management is essential for industrial upgrading. However，the hot rolling process is characterized by strongly coupled multivariable factors，along with limited material information sensing，fragmented data systems，and insufficient automation in critical stages. These challenges hinder the coordinated optimization of product quality，rolling stability，and manufacturing cost. This research systematically reviews recent technological advances，with a focus on progress in the following key areas. In the field of multidimensional material information sensing for hot continuous rolling，robust sensing systems based on machine learning and deep learning have addressed the difficulty of feature extraction under high-temperature and high-noise conditions，substantially improving information acquisition in complex conditions. In the area of multi-zone operational interconnection and edge–end collaboration，the integration of heterogeneous data from multiple sources and the use of dynamic resource allocation mechanisms have eliminated information discontinuities among storage yards，rolling lines，and roll grinding systems，enabling coordinated optimization across regions. In centralized control of multiple rolling lines and regions in hot continuous rolling，intelligent rolling technologies oriented toward reduced manual intervention have been developed by leveraging advanced inspection systems and high-accuracy hybrid modeling，thereby enhancing the compactness and responsiveness of the production process. In cross-business coordination for hot continuous rolling，lean management platforms integrating online prediction，real-time monitoring，and anomaly diagnosis have been established within a deeply integrated cyber–physical framework，driving transformation in production management across multiple business domains. Finally，the paper summarizes current developments and outlines future directions for efficient large-scale production and refined quality management of wide hot-rolled strip.

This research establishes the theoretical framework for the kinematic analysis and synthesis of real mechanisms, defines the concepts of real mechanisms, real links, and real kinematic pairs, and develops methodologies for the kinematic analysis and synthesis of real mechanisms. Firstly, based on measured motion data from real revolute/prismatic pairs, the discrete trajectory properties of their constrained geometric elements are analyzed. This reveals five global motion invariant parameters: the translational displacement region of the minimum quasi-line point and the angular displacement region of the minimum spherical image orientation. It is shown that real kinematic pairs do not possess ideal axes; instead, approximate moving and fixed quasi-axes are determined by the overall motion properties, thereby defining the dimensions of real links. Secondly, the motion of real kinematic pairs is decomposed into the transport motion of the moving quasi-axis and the relative motion about this axis. Crucially, the transport motion parameters correspond to the motion invariants of the moving quasi-axis. This leads to the proposal of the invariant motion accuracy for real kinematic pairs. Thirdly, a real mechanism is formed by connecting the moving and fixed quasi-axes of real kinematic pairs via real links. Fundamental kinematic equations for real mechanisms are established, revealing the intrinsic relationships between the mechanism’s structure, link dimensions, and its real motion. Finally, a framework is constructed encompassing two processes: kinematic analysis, which solves for the output motion patterns given the mechanism’s structure, dimensional parameters, and motion invariants; and kinematic synthesis, which identifies or estimates the structural parameters and motion invariants from experimental test data. These methods provide the theoretical basis and methodology for analyzing, designing, and compensating for motion accuracy and errors in machine tools. Consequently, this work extends mechanism kinematics theory from rigid-body mechanisms to real mechanisms, broadening the application scope of mechanism science and offering novel pathways for enriching and advancing mechanism theory.

As the actual surfaces of manufactured parts are inevitably different from the designed ideal ones, how to quickly and accurately establish a model that can describe such differences as geometric defects, has always been challenging in tolerance analysis. Current geometric defect models usually simplify geometric errors into rigid translation and rotation of ideal surfaces, ignoring the influence of surface topography on the relative position among parts, which in turn restricts the accuracy of tolerance analysis results. As a model of geometric defects based on the discrete geometry framework, skin model shapes (SMS) enable to reflect the geometric deviations more realistically in tolerance analysis. Therefore, the emergence of SMS has attracted widespread attention from scholars. However, the current research on SMS is characterized by a lack of systematic organization, and there is no consensus regarding its relative importance. This study systematically reviews the origin of SMS. The characteristics and connotations of SMS are further clarified. A technical framework of SMS technology is proposed, extracting three technical issues of SMS, including the definition of non-ideal surfaces, the law of geometric error distribution and the mechanism of multi-physical fields. Three types of methods, in the context of feature operations, geometric error modelling and multi-physical field characterization, are summarized as key enablers in the framework. Then, existing modelling methods for geometric defects and their typical applications are analysed in detail. Three core challenges currently faced by SMS at the theoretical level, technical level and application level are pointed out. Finally, three directions for the future development of SMS technology are highlighted.

The rail bogie is recognized as an important power transmission unit in high-speed trains, and the bogie bearing, serving as a load-bearing element within it, is subjected to prolonged alternating loads and is highly prone to failure. Existing frequency domain analysis methods for rolling bearing fault diagnosis are limited by strict applicability conditions. For example, the fast kurtogram method often fails when analyzing signals contaminated by interference or excessive background noise, significantly limiting the widespread practical application of those methods. To address these limitations, a fault diagnosis and decision-making method based on multi-spectrum fusion evaluation is developed. A weighted evaluation criterion is constructed for frequency domain analysis methods, considering three aspects-diagnostic effect, stability, and calculation time-to quantify their engineering suitability. Furthermore, a spectrum evaluation criterion is established to assess the diagnostic effectiveness of frequency domain analysis methods. To accommodate the distinct requirements of data accumulation and completion phases in high-speed rail operation, the proposed spectrum evaluation criterion incorporates downscaled spectral feature fusion and an optimized Fisher's discriminant ratio, respectively. This facilitates the selection of the fusion spectrum yielding optimal diagnostic performance. The validity of the method is confirmed through data collection experiments. The results demonstrate its good engineering applicability and robustness.

Large-scale lattice structures, characterized by their high porosity and multifunctional properties such as impact resistance, vibration damping, and noise reduction, hold great potential in key industrial sectors including national defense, marine engineering, and construction. However, limitations in current manufacturing technologies and methods remain a critical bottleneck for their broader application. Additive manufacturing (AM), by virtue of its layer-by-layer fabrication principle, overcomes the constraints of traditional techniques and enables the integrated 3D formation of spatially complex large-scale lattice structures. This study focuses on the latest developments in AM technologies and approaches tailored for large-scale lattice structures, and provides a comprehensive review from three perspectives: manufacturing processes, manufacturing equipment, and application prospects. In terms of manufacturing processes, we compare the state-of-the-art fabrication methods for large-scale multi-material metal lattice structures developed by domestic and international research teams, with a focus on truss-type and regular geometric lattice structures. Regarding manufacturing equipment, we summarize the structural configurations and control systems of current AM platforms, highlighting key differences and commonalities. Finally, we explore the future application potential of large-scale lattice structures in the context of AM. This review aims to systematically elucidate the recent progress and future directions of large-scale lattice structure fabrication via additive manufacturing, and to promote the industrial application of AM technologies for the efficient and high-quality production of lightweight structures.

7075Al alloy is an important industrial material due to its excellent mechanical properties and corrosion resistance. But its high alloy element content results in poor weldability, which limits its wide application in complex connection scenarios. Therefore, the effective welding of 7075Al alloy is of great significance for its further application. The metal transfer behaviors and weld formations in AC-CMT arc welding of 7075 Al alloy with ER7075 and ER5356 wire were comparatively investigated using a synchronized acquisition system for process parameter and image. In the EN (electrode negative) half cycle, the droplets at the ends of both types of welding wires are in a regular elliptical shape, and the transfer processes are both stable without obvious spatter. In EP (electrode positive) half cycle, the molten droplet at the end of ER5356 welding wire takes a pear shape, and is transferred into the weld pool with slight spatter. The droplet at the end of ER7075 welding wire is irregular and elongated, and transferred into the weld pool with increased spatter. The elongated droplet vibrates up and down, which is prone to cause premature contact with the vibrating molten pool surface, resulting in abnormal short circuit. The intense and ununiform Zn evaporation on the droplet enhanced by the anode spots inherently occurring on the metal evaporation sites is the main reason for the increased spatter in EP half cycle. On the other hand, the Zn evaporation causes noticeable arc contraction, reducing the weld fusion width and increasing the penetration capacity. The weld made with ER7075 wire is quite good-looking with fine fish scale patterns, although it is covered a dark oxide film and with much more tiny spatter particles scattered nearby. The tensile strength of the T6 heat-treated joint welded with ER7075 wire is 117.8% higher than that welded with ER5356 wire.

The shape, performance and function of 4D printed shape memory polymer functional components can be controlled by time or space under specific external excitation, and simple structure can be transformed into a three-dimensional structure with complex geometry to realize specific function and performance characterization. The design of 4D printed shape memory polymer functional components is one of the key factors affecting function realization and performance characterization. This study summarizes the research progress of 4D printed shape memory polymer functional components from the aspects of design principles, design methods and process parameters. The outstanding achievements and innovative technologies of material component design, programmable structure design and preparation parameter design of 4D printed shape memory polymer are analyzed and discussed. The engineering applications of 4D printed shape memory polymer functional components in the fields of electronic devices, medical devices and soft robots are introduced. Finally, the challenges faced by 4D printing shape memory polymer functional components are summarized and the development trend of 4D printing design in the future is prospected.

The underwater welding process is influenced by various factors, including water intrusion, cooling effects, and their impact on the microstructure and properties of the weld joint. To investigate the effects of the underwater environment on the performance of 304 stainless steel partial dry laser welding joints, experiments were conducted using a self-designed double-layer gas drainage device for local dry underwater laser welding of 304 stainless steel at a water depth of 50 mm. The underwater and terrestrial welds were studied under laser power of 3.0 kW, welding speed of 0.8 m/min, and inner gas flow rate of 25 L/min. The results indicated that water intrusion during the welding process caused partial oxidation of the weld seam surface. The cooling effect of water reduced the peak temperature and cooling rate during the welding process, leading to an increase in nucleation rate and undercooling, promoting grain refinement, and altering the solidification mode, resulting in a microstructure consisting of austenite and partial ferrite after solidification. The average hardness of the underwater joint was higher than that of the terrestrial joint, and the tensile strength could reach 97% of the terrestrial joint. Fracture morphology and mechanism analysis revealed that both joints exhibited ductile fracture with micro-pore aggregation, while the underwater joint exhibited some brittle characteristics. Moreover, the cooling effect of water exacerbated the segregation of Cr and Ni elements, reducing the corrosion resistance of the underwater joint.

Production scheduling remains a perpetual research hotspot in industry, serving as a critical metronome for the efficient operation of production lines. With the continuous evolution of intelligent manufacturing, smart production scheduling has emerged as a cutting-edge frontier. Multi-source stochastic disturbances, such as production task variations, the coupling of manufacturing resources, and others, pose a significant challenge in balancing scheduling efficiency and accuracy during dynamic production. To address this challenge, a real-time scheduling simulation optimization method based on digital twin (DT) and reinforcement learning (RL) is proposed. DT technology is used to construct high-fidelity models of production lines, establishing a hierarchical and high-fidelity virtual production simulation environment. An improved Q-Learning algorithm is developed to establish a scheduling optimization agent, incorporating triple state space reconstruction, a multi-dimensional reward function, and a dual exploration strategy to mitigate the curse of dimensionality and the robustness limitations inherent in traditional algorithms. Furthermore, a hierarchical execution control architecture is established based on perception-decision-execution loop throughout the production simulation process to achieve deep fusion between the DT and the intelligent simulation agent. A case study focusing on aerospace product final assembly line is provided to demonstrate the effectiveness of the proposed method. The result shows that the execution distances yielded by five other classical scheduling rules are 6.38% to 16.50% higher than those of the proposed method, signifying a substantial improvement in manufacturing resource collaborative efficiency.

In order to address the challenges which negatively impact surface quality and efficiency encountered during the machining of difficult-to machine materials titanium alloys, such as cutting heat accumulation and tool wear, the ultrasonic-assisted milling (UAM) technology based on physical vapor deposition (PVD) coated tungsten carbide tools has been proposed for efficient and high-quality machining of titanium alloys. Firstly, the vibration separation mechanism at the tool-workpiece interface in UAM is analyzed. Subsequently, the effects of process parameters, including spindle speed and ultrasonic amplitude, on the surface quality of titanium alloy TA15 are investigated, and the process parameters are optimized. Finally, a comparative analysis is conducted between conventional milling (CM) and UAM to investigate the wear mechanisms of PVD-coated Zr-based and Ti-based tools during titaniumalloy milling, as well as the influences of UAM process parameters on the surface quality of titanium alloy. The experimental results indicate that the proposed UAM based on PVD-coated tools may realize efficient and high-quality machining of titanium alloys. The optimized process parameters of UAM are determined as the spindle speed of 10 000 r/min and the ultrasonic amplitude of 3.30 μm. The wear forms of the coated tools mainly include coating peeling, crater wear on the rake face, as well as adhesion, diffusion and oxidation wear on the flank face. Additionally, ultrasonic vibration can reduce the diffusion, oxidation, abrasive wear of the tool and the micro-chipping of the cutting edge. In comparison to CM, the adoption of UAM leads to a reduction of approximately 15% in surface roughness of titanium alloy. At the same time, compared to Ti-based coated tool, the average wear volume on the flank face of the Zr-based coated tool is reduced by 18.34%. The milled surface roughness of titanium alloy is 0.082 μm, which is hardly any adhesion of titanium chips.

The broken wire of wire rope in service presents the characteristics of complex spiral distribution in space and close proximity in space, which makes the detection of wire rope broken wire difficult and low in accuracy. Therefore, the research on non-destructive testing of wire rope breakage based on magnetic aggregation technology and three-dimensional magnetic flux leakage testing(MFT) is of great significance to improve the accuracy of wire rope breakage detection and ensure the safety of wire rope bearing. Based on the 3D MFT principle and magnetic aggregation technology, Maxwell finite element software and test platform were used to carry out finite element analysis of magnetic flux leakage(MFL) field and detection test of broken wire under different wire breaking modes(broken length, broken number and broken position). The variation law of MFL field on wire rope breaking by magnetic aggregation concentrator and the evolution law of MFL signal characteristics under different wire rope breaking modes are revealed. The results show that the magnetic aggregation concentrator has a gain effect on the magnetic flux leakage detection of wire rope broken wires, and the change of the magnetic flux leakage signal at the center of the aggregation concentrator is the most significant.; There is a positive correlation between the 3D MFL signal of wire rope breaking and the characteristics of wire rope breaking(length of wire break, number of wire break). The greater the change of wire rope breaking characteristics, the more significant the change of MFL signal. The sensitivity of MFL components in different directions to the wire rope breaking characteristics is as follows: axial component > radial component > circumferential component; When there is the same number of broken wire inside and outside the wire rope, the signal change is more significant when the wire is broken outside the wire rope. The MFL field is superimposed when the wire rope is adjacent to the broken wire, and the 3D MFL field components increase significantly(compared with that of a single broken wire). When the wire rope is broken at multiple circumferential places, the closer the broken wire is to the permeability boss, the more significant the change of broken wire signal is.