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.

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.

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.

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.

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-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.

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.

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 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.

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.

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 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.

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.

Digital technology has been widely applied in the development of aerospace products, promoting the continuous development of aerospace system engineering methods. In order to further clarify the development direction of aerospace system engineering in the digital age and promote digital space construction, this article provides an overview of the development and application of aerospace system engineering driven by digital technology. Firstly, the new characteristics of the development of aerospace system engineering in the digital age were analyzed from three aspects: model-based system engineering development, digital intelligence technology empowerment, and commercial aerospace promotion. Then, typical full lifecycle system engineering theoretical models in the digital age were analyzed, and the development of digital technology integration applications was analyzed from the entire development process scenarios, including planning and demonstration, development and design, production and manufacturing, experimental testing, service guarantee, and production management. The characteristics of establishing a full lifecycle development and management digital ecosystem through the integration of technologies such as MBSE, digital twin, digital thread, and artificial intelligence were elucidated. On this basis, combined with the core scenario transformation goals of aerospace product development and management, the future technological development trend of digital era aerospace system engineering is proposed, in order to further promote the improvement of the theoretical and methodological system of aerospace system engineering and provide support for the construction of digital development and management mode.

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.

Conformal electronics on curved surfaces offer unique advantages in achieving multifunctionality and high integration of electronic devices by directly adhering to target substrates, demonstrating significant potential value in fields such as wearable electronics, biomedical devices, and aerospace engineering. To systematically address the challenges of poor printing flexibility, low printing accuracy, and post-processing limitations in the fabrication of conformal microscale circuits on complex curved surfaces, this study proposes an integrated manufacturing method combining multi-degree-of-freedom aerosol jet printing and in-situ laser sintering based on a six-axis robotic arm. The robotic arm is equipped with an aerosol jet printing module and an infrared laser sintering unit, and a multi-threaded control architecture is developed to enable flexible path planning and synchronous control of printing parameters. To achieve uniform sintering of aerosol jet printed conformal microscale circuits on curved surfaces, a multi-degree-of-freedom conformal in-situ laser sintering methodology is proposed, yielding optimized laser sintering process parameters. This enables high-precision printing and high-conductivity sintering of conformal circuits on surfaces with curvature variations of 25~100 m^-1 and a large area of 400 mm×190 mm. The circuit line width is less than 50 μm, and the electrical resistivity is as low as 6.53×10^-8 Ω·m. Furthermore, the proposed integrated process is validated to be compatible with polymer substrates with heat deflection temperatures ranging from 60 ℃ to 360 ℃. The study has successfully achieved direct manufacturing of conformal conductive circuits on a spatial double-helix surface across a wide inclination angle range of -20° to +30°, and also realized 8-channel conformal thermal sensor circuits on a curved blade model surface. This study overcomes key technical bottlenecks in the flexibility and functionality of conformal microcircuit manufacturing on complex curved surfaces, providing an efficient and reliable solution for the rapid fabrication of conformal electronic systems.

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.

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.

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.

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.

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.

There are significant challenges in balancing the strength and plasticity in traditional alloy triply periodic minimal surface (TPMS) lattice structures fabricated by laser powder bed fusion (LPBF). In this work, a systematic study on the LPBF process of AlCoCrFeNi_2.1 eutectic high entropy alloy and its TPMS lattice structures' mechanical properties has been conducted. The optimal parameter combination resulted in the elastic modulus of 255 GPa, the yield stress of 1348 MPa, the compressive strength of 2520 MPa, and the compressive strain exceeding 25%. Microstructure characterization revealed a dual-phase nanolamellar structure of FCC (130-250 nm) and BCC (20-30 nm) with element segregation forming heterogeneous interfaces for synergistic strengthening. Diamond, Gyroid, Primitive TPMS structures and BCC truss structures are fabricated to reveal the influence of lattice configuration, relative density, and unit cell size on quasi-static compression properties. The elastic modulus, yield stress, and energy absorption are positively correlated with the relative density. The maximum energy absorption of Diamond, Gyroid, and Primitive structures reached 2369 J, 2062 J, and 1096 J, respectively. The elastic modulus and yield stress increased linearly with unit cell size, and the plateau stress and energy absorption are enhanced simultaneously. At a relative density of 40%, the specific elastic modulus of Gyroid and Primitive structures reached 47.8 GPa/kg and 46.9 GPa/kg respectively. The Diamond structure had the best comprehensive properties with a specific elastic modulus of 72.6 GPa/kg and a specific energy absorption of 38.7 J/g. Compared with TPMS lattices of 316L stainless steel and Ti-6Al-4V titanium alloy, the AlCoCrFeNi_2.1 lattice structure has superior strength-plasticity matching and great application prospects for high load-bearing and energy absorption under extreme loading conditions.

With the rapid development of aerospace technology, the multifunctionalization of launch vehicles has become an important development direction for the future aerospace transportation system. The research focuses on mechanism technologies that enable the multifunctional development of launch vehicles, and systematically explains connotations, key technologies and challenges. First, the demand background and development trend of launch vehicle multifunctionalization are analyzed, and mechanism technology is proposed to be the core support for achieving multifunctionalization. Secondly, the process of relevant mechanism technologies accompanying the development of China launch vehicle and the characteristics of professional development of mechanisms at different stages are sorted out, and the launch vehicle mechanism entered a new stage of system integration development is proposed. Then, in order to explore the future development of mechanism technology, This research set up a two-stage reusable launch vehicle lunar landing mission scenario, and analyze the new development needs of landing mechanisms, pneumatic mechanisms, servo mechanisms, deformation mechanisms, separation mechanisms and new mechanisms in the entire mission profile. Finally, new opportunities and challenges faced by mechanism technology in solution innovation design, performance system verification, service reliability assessment, and intelligent development are discussed. Research shows that breakthroughs in mechanism technology will improve the mission adaptability and economicality of launch vehicles and promote the development of aerospace transportation systems to a more efficient, reliable, and economical direction.

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 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.

A multi-dimensional quasi-zero stiffness vibration isolation method combining flexible suspension and inverted pendulum is proposed to meet the requirements of ultra-low frequency vibration isolation for precision equipment mounted on aerospace launch vehicles and other aircraft. By connecting the flexible suspension to specific positions below the centroid of payload, the horizontal quasi-zero stiffness is achieved through the pendulum effect of flexible suspension and the inverted pendulum effect caused by upward centroid shift of payload. The vibration equation of the flexible suspension-inverted pendulum composite isolation system is established, and its multidimensional vibration isolation characteristics are analyzed. Key parameters including suspension stiffness, horizontal and vertical positions of suspension points relative to payload centroid are investigated for their influences on system stability, natural frequencies, and vibration transmissibility. Results indicate that the stability of isolation system decreases with reduced suspension stiffness, increased payload centroid height, and decreased horizontal spacing of suspension points, requiring rational parameter configuration to ensure stability. For given payload, vertical isolation performance mainly depends on suspension stiffness, while horizontal high-frequency isolation performance is primarily determined by suspension working length. Reducing suspension stiffness and suspension point spacing while increasing payload centroid height can improve low-frequency horizontal isolation performance. The coupling between horizontal translation and rotational motion creates an anti-resonance phenomenon near the main rotational natural frequency, enabling excellent horizontal isolation in specific frequency bands. A prototype is developed and tested for natural vibration characteristics and vibration transmissibility, with experimental results verifying the theoretical analysis.

During the process of high-speed trains service, different degrees of low-frequency hunting motion always occur and large-amplitude of hunting motion can significantly deteriorate the wheel-rail rolling-contact relationship and threaten vehicle-operation safety. Based on the vehicle-track coupled dynamic theory, the high-speed train-track spatial coupled dynamic model was established in this paper, in which the non-Hertzian wheel/rail rolling-contact model was considered. With an adaptation of the numerical model, the wheel-rail interaction mechanism of high-speed trains under hunting conditions with different wear wheel/rail profile matching states was systematically compared. The results show that under the hunting conditions the dynamic stress distribution of high-speed trains vibrates periodically and wheel-rail contact-stress distribution under the hunting condition is significantly higher than that under the steady-state operation condition. The adhesion characteristics of the wheel and rail under hunting conditions are much worse than those under steady-state operation condition, and the adhesive region will decrease as wheelset lateral displacement rate is increase. At the same time, the wear-depth distribution under the hunting conditions is much higher than that under the steady-state operating conditions.

The intelligent vehicle cyber-physical system (IVCPS) is a system-of-systems with evolving and emergent properties, demanding robust architectural design. To develop a feasible, verifiable, and scalable IVCPS architecture, the Scenario-oriented Model-and-Simulation-based System-of-Systems Engineering approach is proposed. This method examines IVCPS’s structure and evolution, adopting the Unified Architecture Framework Modeling Language to build a distributed framework maintaining consistent models across scenario task systems. The architecture is developed from seven viewpoints and validated via simulations. A standard model library is established to enable quick adaptation to new scenarios, while integrated multi-resolution simulations ensure positive system emergence. The system layer adopts MBSE for modeling and simulation. A case study of the demonstrates the first-stage IVCPS architecture. Simulations validate its logic and parameters, confirming the capacity to address system development needs, facilitate design transitions, identify hidden collaborative requirements, and quickly assemble new scenario architectures.

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.

As core assets in modern military and civil aviation, aviation equipment features high complexity, long service lifecycles, and stringent reliability requirements, posing severe challenges to equipment development and operation maintenance. Digital twin technology provides effective support for the full-lifecycle management of aviation equipment. However, conventional digital twin technologies remain confined to static representation, making them insufficient to support deep cognition, evolutionary prediction, and intervention-oriented decision-making for the complex and dynamic behaviors of equipment throughout its lifecycle. In this context, driven by rapid advances in cognitive intelligence technologies such as large models, knowledge graphs, and causal reasoning, cognitive digital twins are emerging as a new research frontier. Existing studies on cognitive digital twins are largely limited to localized explorations within specific scenarios. Their theoretical system remains underdeveloped, and a unified architecture for complex aviation systems is lacking. In view of these gaps, a novel cognitive digital twin framework for the full-lifecycle management of aviation equipment is proposed, aiming to elucidate the paradigm shift from state mapping to autonomous cognition. First, it reviews the evolution of digital twin technology from “virtual-physical mapping” to “intelligent cognition”, and analyzes the current applications and urgent needs of this transition in the aviation domain. Second, the cognitive digital twin framework is deconstructed into its constituent layers—physical entity, virtual entity, interactive connection, and cognitive embedding. It also provides a detailed explanation of the cognition-enabling mechanisms of cognitive digital twins across the full lifecycle of the equipment. Subsequently, it systematically summarizes the research status and technical pathways of each layer from an implementation perspective. Finally, in response to current challenges faced by the digital twin application of aviation equipment, it explores the integration of emerging technologies including large models, and looks ahead to future trends in constructing a cognitive digital twin system for aviation equipment with deep comprehension, autonomous evolution, and trustworthy decision-making capabilities.

As a new type of heavy-duty equipment for large cargo transportation, the cooperative traction articulated heavy-duty vehicle（CTA-HDV） is composed of a front and a rear traction unit connected to the cargo through an articulated device. Compared with traditional heavy-duty vehicles, its modeling and stability analysis are more difficult. A dynamic modeling method and stability analysis scheme for CTA-HDV system in the horizontal plane is proposed. In terms of system modeling, based on kinematic and dynamic analysis, the dynamic coupling relationship at each articulated point is analyzed, and a 4-degree-of-freedom single-track model of the cooperative traction articulated vehicle is established. In terms of stability analysis, first, the handling stability of CTA-HDV system is analyzed by independently defining the understeering gradient of traction unit 1 and traction unit 2. The influence of vehicle parameters on the understeering gradient of the CTA-HDV system is studied. The results show that the relative position of the center of mass of traction unit 1 and the relative position of the articulated point between traction unit 1 and the intermediate rigid body have the greatest influence on the understeering gradient of the system. When is adjusted from 0.3 to 0.6, the understeering gradient of the system decreases by approximately 79%. When is adjusted from 0.6 to 1.2, the understeering gradient of the system increases by approximately 86%. Secondly, combined with the dynamic stability analysis method of articulated vehicles, the dynamic critical speed of the CTA-HDV system is studied, and it is found that the dynamic critical speed of the CTA-HDV system is around 100 km/h. The influence of structural parameters on the dynamic stability of the system is also analyzed. This study provides a theoretical basis for the safety design of the CTA-HDV system.

Problems with out-of-round wheels are widespread in subway trains. They can result in impact loads between wheels and rails, increase vehicle vibrations and noise, and cause fatigue fractures of vehicle/track components, which has a significant effect on ride comfort, operational safety, and wheel maintenance. The out-of-roundness test data of more than 9,000 wheels from 21 subway lines in China are statistically analyzed, covering four types of subway trains with maximum speeds of 80, 90, 100, and 120 km/h. The characteristics of wheel out-of-roundness such as radial run-out, orders, and wavelengths are determined. Welch’s method is used to estimate the spectra of all measured out-of-roundness data of the wheels. Then, the two-segment power function is used to obtain the roughness spectra, which are compared with the roughness spectra of high-speed wheels. The results show that the subway wheels have out-of-round problems to varying degrees, with about 10% of the wheels having radial run-out greater than 0.5 mm. The order of wheel polygonal wear is mainly 1st order (eccentric), 5th to 9th, and 12th to 16th order. The dominant wavelength of high-order polygonal wear (more than 10th order) is in the range of 160-200 mm in the 1/3 octave band. Compared with the roughness spectra of high-speed wheels, the roughness spectra of subway wheels are higher. The results of this paper can be helpful in the evaluation, maintenance, and repair of subway wheels in out-of-round conditions, as well as provide important information for dynamic wheel-rail interaction and wheel-rail noise simulation.

Addressing the drag and heat reduction requirements of hypersonic vehicles under "long-endurance, high-Mach-number, high-overload, reusable" operating conditions, this review focuses on active mass injection structures. It systematically surveys research advances in two types of porous structures – discrete injection holes and micro-permeable porous configurations – and thoroughly analyzes their fluid-thermal coupling mechanisms and adaptability to extreme environments. The study critically evaluates advanced manufacturing processes, including metal/ceramic porous material systems and additively manufactured porous structures, while summarizing their engineering applications in critical components such as internal high-temperature engine parts and windward-facing vehicle surfaces. Furthermore, it synthesizes lightweight design strategies like self-pumping, zoned regulation, and active-passive hybridization, and identifies cutting-edge development pathways: matrix material intelligence, design-process intelligence, and verification-system intelligence. This work provides valuable insights for overcoming extreme thermal barriers and regulating frictional drag in hypersonic vehicles.

Flexible robots with multiple degrees of freedom can be driven to adapt to various shapes and complex environments with lower risks for their inherent flexibility. Robotics development has become one of the hot spots since its broad applications in many fields, such as biomedicine, pipeline maintenance, and rescue, are prospected. However, the low stiffness brought by flexibility makes it difficult to support and manipulate objects under high load conditions. Inspired by natural creatures, researchers introduced adjustable stiffness elements and materials into flexible robots to switch between rigid and flexible states alternately, which provides an effective method to solve the potential contradiction between compliance and high stiffness. This paper presents a systematic review of the latest advances in research on stiffness modulation for flexible robots, including the methods used for stiffness modulation and program configurations. The stiffness modulation principles are classified as intelligent material-based stiffness modulation, structure-based stiffness modulation, and interference-based stiffness modulation. At the same time, the combination of various stiffness modulation methods, especially the variable stiffness strategy combining interference structure and origami technology, is introduced. With the development of material science and mechanical structure technology, adjustable stiffness flexible robots with high load-carrying capacity, high flexibility, and lightweight will become an important research direction in future robotics.

Hydroforming equipment is widely used in large tonnage, large size stamping, forging and other forming processes. Hence, high performance leveling system is needed to ensure the forming accuracy of the equipment. However, the traditional diagonal couple leveling system and its identity control strategy cannot accurately eliminate the inclination torque, which affects the forming quality of the workpiece. To solve the above problems, a four-corner leveling electro-hydraulic system based on independent metering for high-speed hydraulic presses is designed. In terms of leveling control, a diagonal cross and mean coupled synchronous control strategy based on pump-valve coordinate control is proposed. For the meter-out displacement controller design, the tracking error of each leveling cylinder is taken as negative feedback input, and the mean of tracking error and diagonal error are taken as compensation input into the sliding mode controller, so as to achieve accurate tracking and synchronous control of each cylinder. For the meter-in pressure controller design, the pressure servo valve is adjusted according to the working mode at the left and right maximum opening, so that the flow into the leveling cylinder is maximum and the pressure is minimum. For the pump flow controller design, the motor speed is determined according to the working mode of the leveling cylinder. Finally, an experimental platform for four-corner leveling of hydraulic press was built. The experimental results show that compared with the traditional identity control strategy, the proposed leveling control strategy improves the synchronization accuracy by 9.26%. At the same time, due to the energy-saving characteristics of the independent metering system, the power consumption of the system is reduced by 54% under the action of the control strategy. This study shows practical significance for the research and development of leveling system of high-speed forming equipment.

In order to analyze the influence of streamline length on aerodynamic drag of high-speed train, based on computational fluid dynamics, the aerodynamic drag distribution characteristics of 400 km/h high-speed trains running on open lines are numerically simulated, the aerodynamic drag of train models with different streamline lengths is predicted. The reliability of the numerical simulation is verified by the wind tunnel test. Under the condition of the same length of the whole train, the influence of the streamline length of the head and tail car on the aerodynamic drag of the streamlined head, non-streamlined car body and bogie is studied. The length of the streamline is 10-15 m. The economic benefit relationship between the positive benefit of aerodynamic drag reduction and the negative benefit of passenger space reduction caused by the increase of streamline length is discussed. The results show that the resistance of the streamlined area of the head car is positively correlated with the ratio of its surface area to the streamline length. The resistance per meter in the non-streamlined area of the head car decreases gradual to the central area of the train, and the resistance per meter of the middle car decreases slowly to the direction of the tail car. The resistance per meter of the non-streamlined part of the tail car gradually converges; the streamline resistance of the tail car decreases with the length of the streamline. When the streamline length is about 13 m, the maximum benefit of 49.88 yuan/km is achieved, and the aerodynamic drag of 700 N is reduced, the benefit is optimal.

China’s aerospace manufacturing has gone through research and development dominated phase, development and production mixed phase, and now is entering a new phase of mass customization production driven by normalized high density emission of aerospace products, which calls for a new manufacturing mode that can simultaneously meet the major national strategy and the characteristics of China's aerospace industry. This article analyzes the direction of transformation and development in aerospace manufacturing, proposes the new aerospace manufacturing model named as final assembly pull, which the basic connotation and main characteristics are elaborated. This study proposes an implementation framework that includes four aspects: overall manufacturing control, efficient flexible assembly, complete supply chain management, and network collaborative manufacturing. It is committed to building a new aerospace product manufacturing system, achieving efficient collaboration among multi-level, multi legal person manufacturing enterprises across different regions, different networks, and different systems, improving the ability to deliver high-quality, efficient, and low-cost aerospace products, and cultivating new quality productivity in aerospace industry. The proposal of the aerospace final assembly pull manufacturing model could provide important support for the completion of major aerospace missions and the transformation and upgrading of the aerospace industry.