Industry 5.0 is an emerging manufacturing paradigm driven by value, with human-centric intelligent manufacturing as one of its core concepts. However, research on human-centric intelligent manufacturing is mainly focused on system level, and there is still limited research on its application of specific processes such as additive manufacturing. Therefore, there is an urgent need to clarify the relevant scientific issues and key challenges. A reference framework is proposed for human-machine collaborative additive manufacturing aligned with Industry 5.0, based on the theory of the human-cyber-physical system. It establishes a three-level model encompassing product, economy, and ecology, and elucidates the fundamental concept of human-machine collaborative additive manufacturing, taking into account the intrinsic features and functional evolution of additive manufacturing technology. Focused on the product development process, key enabling technologies are discussed, including the Internet of Everything, artificial intelligence, digital twin, augmented reality, and smart materials. These technologies facilitate typical applications in different layers of the framework, including customized product design, interactive manufacturing, human-machine collaboration for product chains, crowdsourcing, distributed manufacturing, and energy saving are discussed. The objective of this framework is to study human-machine collaborative additive manufacturing and systematically present its core concepts, key technologies, and typical scenarios, thereby promoting the transition of additive manufacturing towards the Industry 5.0 paradigm of human-machine collaboration and better meeting user's personalized demands.

As engine performance requirements continue to improve, the operating conditions of the fuel control system have become harsher and the boundary conditions have become more complex. There are various causes of fatal failures in fuel control systems,including inherent pressure pulsation of the fuel pump and fluid-solid coupling vibration of pipelines and valves, leakage caused by corrosion or aging of sealing rings, increased wear due to oil contamination or lubricating oil failure, etc. At the same time, the fuel control system has the characteristics of few measuring points, variable operating conditions, strong interference, and strong nonlinearity. Therefore, there is an urgent need for fault diagnosis technology in this field while facing huge challenges. In order to promote the development of fault diagnosis technology in the field of fuel control systems, this study reviews the main methods and classifications of fault diagnosis technology after summarizing the characteristics and common faults of the fuel control system.Furthermore, from the perspective of hydraulic component interchangeability, the current research status of key components of fuel control systems is summarized based on physical models, signal processing and artificial intelligence diagnostic methods. Finally, the challenges and opportunities existing in fuel control system fault diagnosis technology are pointed out.

Surrogate model is a substitute of the numerical analysis model with complex and time-consuming calculation, which can make up for the shortcomings of traditional engineering optimization design and numerical simulation analysis, such as long design cycle, high computing costs, and curse of dimensionality. Surrogate model technique has developed rapidly and achieved abundant outcomes in recent years. It has been gradually applied in aerospace, ship industry, vehicle, power generation and other fields. First, this paper gives a general overview of the surrogate model technique, and investigates the total and increment number of papers published on the surrogate model in the past thirty years. Second, the research status of surrogate model and related typical methods and theories are introduced in detail from three aspects, namely design of experiments, single-fidelity surrogate model and multi-fidelity surrogate model. Then applications of surrogate model in optimization design are investigated. The overall investigation of the surrogate-assisted multidisciplinary, surrogate-assisted multi-objective, and surrogate-assisted uncertainty optimization design techniques are analysed and summarized. Finally, the underlying problems existing in the surrogate model technique are summarized, and some suggestions are proposed for the future research.

The development and application of advanced electric vehicles has become the key technology to achieve “decarbonization”. Accurate state of health(SOH) prediction of battery can effectively characterize its operation performance. It is of great significance to the maintenance and life management of battery in electric vehicle. In recent years, a new generation of artificial intelligence technology represented by deep learning, reinforcement learning and big data technology has become a research hotspot in the application of battery state prediction. The basic theory of artificial intelligence technology and SOH and SOH influence factors is briefly introduced. Several main artificial intelligence algorithms in SOH prediction are summarized and discussed from the perspective of battery cell and battery system respectively. Finally, combined with emerging technologies such as big data, cloud computing and regional chain, some battery SOH prediction problems are discussed, which provides some ideas for breaking through the bottleneck of current power battery full life cycle management technology.

Since hemispherical resonator gyroscope (HRG) is characterized with simple structure, high accuracy, high reliability, and long service life, the potential applications of HRG are becoming increasing widespread, including national defense, communication satellite, human spaceflight, astronomical observation, ocean engineering, etc. As the hemispherical resonator is core component of HRG, the processing quality of resonator directly determines the frequency splitting and quality factor of the hemispherical resonator, which then affects the working accuracy and service life of HRG. Therefore, investigating the key properties and manufacturing technology of hemispherical resonator is of great necessity. The working principle and research history of HRG, the frequency splitting and quality factor of hemispherical resonator, and the manufacturing process of hemispherical resonator are reviewed. The research methods and latest progress in frequency splitting and quality factor, the hemispherical resonator ultra-precision grinding and magnetorheological polishing process are emphatically analyzed. What's more, the challenges and problems still to be solved in the course of future development are discussed. By summarizing what is mentioned above, references are provided for subsequent works, such as accurate calculation and measurement of the frequency splitting and quality factor of hemispherical resonator, the optimization of hemispherical resonator manufacturing process, and the improvement of HRG navigation accuracy.

Axial piston pumps are the heart of the hydraulic system of aerospace, ocean ships, engineering machinery and other high-end equipment. They deliver transmission blood with stable pressure and flow to the hydraulic system. The reliability and safety of axial piston pumps have direct affects on the performance of the hydraulic system and even the complete machine. Therefore,utilizing intelligent technology to achieve predictive maintenance of axial piston pumps has been a research hotspot in recent years. At the same time, the development of unmanned and intelligent equipment has also emphasized the intelligent technology of axial piston pumps. To explore the development direction of the intelligent technology for axial piston pumps and provide a feasible path for the high-quality development, this study reviews the development history and research status of the intelligent technology of axial piston pumps from four aspects, namely, intelligent condition monitoring, intelligent fault diagnosis, intelligent life prediction and intelligent decision regulation. The existing problems and difficulties of the intelligent technologies for axial piston pumps are explored, the challenges of the existing intelligent technologies are summarized, and the future development trends are forecasted.

Digital twin technology and PHM technology are two hot research directions in the field of intelligent manufacturing. On the basis of summarizing and analyzing the current status of PHM technology, this study summarizes the key issues that currently restrict the development and application of PHM technology, including incomplete research on equipment failure mechanisms,incomplete data throughout the entire lifecycle, insufficient methods for monitoring health status, insufficient synthesis of multi-level status information, and uncertainty management issues. And it elaborates on the unique advantages of digital twin technology in solving these shortcomings, and proposes the construction of multi-dimensional digital twin models based on first principles, multi-dimensional data mapping in virtual and real spaces, efficient iterative correction of twin technology state consistency measurement and model, and system health assessment, prediction, and maintenance decision-making based on multi domain features as key technologies to construct the DT-PHM research architecture. With the continuous advancement and development of technology, the two technologies are constantly deeply integrated. The complex system health management technology based on digital twins will undoubtedly become one of the key technologies for future equipment full lifecycle maintenance and predictive maintenance.

Leakage due to wear is a common form of failure of reciprocating seals during service. At present, the prediction of seal wear life is mainly based on experimental methods, which are complex and have high time and economic costs. Simulation is a better way to achieve the purpose of seal wear life prediction, but currently there is a lack of suitable simulation methods for the important factor of wear-speed. In response to the need for wear life assessment of seal systems for practical engineering applications, a reciprocating seal wear life prediction method considering variable speed conditions is proposed based on the hybrid lubrication model and Rhee wear theory. The evolution of seal performance such as lip contact pressure, contact width, contact deformation and reverse pumping back rate with the accumulation of wear time is simulated by analyzing the change of seal lip profile wear under a given working condition, and the goal of seal wear life prediction is achieved. A comparison of the life prediction with the average speed treatment method is carried out in conjunction with the actual engineering application of the step seal. The results show that the leakage rate of the wear loss cycle under the variable speed treatment method is 3.24 mL/h, and the leakage rate under the average speed treatment method is 0.24 mL/h. There is a significant difference in the prediction results, and the transient change in speed is a factor that cannot be ignored in the reciprocating seal wear simulation speed.

Short arc milling technology is applied to nickel-based superalloy processing due to low machining stress, low cost and high machining efficiency. Aiming at the problem that the evolution law of surface integrity in short arc assisted precision milling is unclear, the microstructure characteristics of the recast layer are analyzed by means of scanning electron microscope (SEM), energy dispersive spectrometer (EDS) and other detection methods. The influence of precision milling process parameters on surface integrity and the difference of milling surface caused by the presence of recast layer are studied by orthogonal milling experiments. The surface strengthening mechanism of short arc assisted precision milling is clarified. The results show that the destruction and recombination of the superalloy crystal at high temperature leads to its microstructure change to form a recast layer, the hardness is reduced by 34%, and the brittleness is enhanced. Under the same parameters, the surface of short arc assisted milling shows higher roughness (Ra=0.76 μm), higher surface hardening rate (47.8%), and larger tensile residual stress (along the feed direction (X direction) is 241.5 MPa, and in the vertical feed direction (Y direction) is 78 MPa). Dislocation strengthening is the main mechanism of machined surface hardening. The microhardness prediction model established by introducing the kernel average misorientation parameter further expands the relationship between surface integrity and crystallographic characteristics.

Product design is a knowledge-evoluted process, which includes the reasoning and transformation of massive complex knowledge. Based on the organization and management of knowledge, knowledge engineering promotes the development of intelligent design systems, so as to improve the quality and efficiency of product design. The research status from five aspects: design knowledge mining, design knowledge expression, design knowledge retrieval, design knowledge recommendation and design knowledge reuse is analyzed, after explaining the connotation of knowledge and knowledge engineering. In view of the problems existing in the product design process, the key techonlogies of knowledge engineering in product design are proposed. For the profound changes brought by the rapid development of emerging information technologies such as Internet, cloud computing, artificial intelligence and big data to product design, the future development trend of knowledge engineering is put forward.

As an advanced structural optimization approach, the essence of topology optimization is to obtain the structural topology configurations with the best performance by seeking the optimal distribution of materials in the design domain under given load and boundary conditions. In recent years, with the continuous development of optimization algorithms and computer science, structural optimization technology has a wide application prospect in aerospace, automobile, ship and other transportation engineering. Based on the theoretical research level, topology optimization approaches and characteristics of discrete and continuous structures are discussed.Then by considering the specific applications of topology optimization in aerospace, automobile, ship and other transportation engineering, the shortcomings of structural topology optimization and the challenges faced by their popularization are pointed out. At the same time, the present situation and development trend of topology optimization industrial software development and application from home and abroad are discussed. At present, the main problem faced by topology optimization is that in the iterative process of large-scale transportation engineering structure optimization, multiple finite element analysis is required which increases the complexity of calculation space and time. By considering the situation, the new discipline direction represented by isogeometric analysis and machine learning to improve the deficiency of topology optimization design is introduced, so as to shorten the design cycle of transportation equipment. and A preliminary discussion on the research trend in the future is also given.

The electroplastic effect stimulated by pulse current can significantly improve the forming ability of titanium alloys. However, the electroplastic effect is the result of the comprehensive action of “electric-thermal-structural” multi-physical fields, and the construction of an electrically assisted finite element model is of great significance for analyzing the plastic deformation behavior and optimizing the forming process parameters. Conducting electric pulse-assisted stretching experiments on Ti-6Al-4V alloy under different current densities, temperatures, and strain rates. Based on the Johnson-Cook model, a constitutive model considering the thermal and athermal effects is established, with the correlation coefficient R² higher than 0.95 and the average relative error lower than 2%. Based on this model, the finite element method analyzes the physical field in the process of electric pulse-assisted stretching. When the current density is 4.19 A/mm2, the current density increased by 21.96% compared with the initial value as the specimen stretched to necking, which led to an obvious increase in the temperature gradient in the gauge section, and the temperature difference increased from 68.69 ℃ to 95.52 ℃ . Compared with the high-temperature test at the same temperature of 350 ℃, the overall uniformity in the strain field is reduced, and the peak stress is reduced by 49 MPa due to the athermal effect. The stress-strain results predicted by the model show a high fitting accuracy compared to the experimental data. It provides a theoretical method for further study of the electro-assisted forming process and electroplastic effect mechanism.

With the development of electronic devices towards high frequency, high voltage and high power, the higher requirements are put forward for the performance of semiconductor materials. Single crystal diamond has the characteristics of high thermal conductivity, high temperature resistance, ultra-wide band gap and high breakdown voltage, which is regarded as the ultimate semiconductor material. To give full play to the properties of diamond, the diamond need possess the surface morphology of super smooth and almost non-damaging. Nevertheless, diamond not only has the highest hardness, stable chemical properties and is difficult to remove, but also has great brittleness, low fracture toughness and easy to produce processing damage, which seriously restricts the development of diamond in the field of semiconductor. The processing mechanism and technology of diamond with high-efficiency and ultra-low damage have been studied by scholars at home and abroad. But up to now, there is no set of processing theory and technology suitable for diamond. On the basis of summarizing the processing methods with their advantages and disadvantages regarding diamond, the research status of diamond processing are summarized, the research progress on the mechanism of high-efficiency and ultra-low damage of diamond and related processes are summarized systematically from the aspects of experimental instrument detection and simulations and finally the current problems and future development trends of diamond regarding the high-efficiency and ultra-low damage processing are analyzed, which will provide guidance for the manufacturing technology and theory of diamond in the future. The analysis shows that, in terms of mechanism, the combination of experimental detection and simulation is an effective way to obtain more details of dynamic atoms, which is the development direction of elucidating processing mechanism at atomic scale in the future. As for processing technology, chemical mechanical polishing method can be used for the large size flat material processing, at the same time also can obtain the diamond surface with super smooth and low damage. Nonetheless, the low removal rate is to limit the bottleneck problems of the application of this method. The processing method consisting of diamond abrasive + strong oxidation agent + external energy field assist can be the development trends of the high-efficiency and ultra-low damage processing of diamond.

Studying driving policies with wide-ranging scenario adaptability is crucial to realizing safe, efficient, and harmonious automated driving. Deep reinforcement learning has shown great potential in driving policy learning with its excellent function approximation and representation capabilities. However, it is extremely challenging to design a reward function suitable for various complex driving scenarios, and driving strategies’ generalization ability needs to be urgently improved. Aiming at the difficulty in designing the reward function, an approximate likelihood model of human drivers’ driving policy is built considering their preferences and a method of learning an approximate posterior distribution over the reward function through sparse action sampling based on curve interpolation and approximate variational inference is proposed, resulting in a Bayesian neural network. Tackling the wrong rewards originate from the uncertainty of a reward function, an uncertainty-aware human-like driving policy learning method based on the posterior distribution over the reward function is proposed, which maximizes the reward while penalizing the epistemic uncertainty. The proposed methods are validated in simulated highway and urban driving scenarios in the NGSIM US-101 and nuPlan datasets. The results show that the proposed method overcomes the problem of poor performance of the reward function based on the linear combination of hand-crafted state features, models the uncertainty of the reward function, and improves the generalization ability of the reward function to high-dimensional nonlinear problems. The learned reward function and the learning stability are significantly better than the mainstream inverse reinforcement learning method. Moreover, penalizing the uncertainty of the reward function improves the human likeness and safety of the driving policy and the training stability. The proposed uncertainty-aware human-like driving policy significantly outperforms the driving policies based on behavior cloning and maximum entropy inverse reinforcement learning.

Robot-assisted surgery aims to assist surgeons in performing surgical procedures through robotic systems, and it has attracted increasing attention in recent years. The rapid development of artificial intelligence (AI) has accelerated the progress of robot-assisted surgery towards minimally invasive, intelligent, and autonomous capabilities. This research provides a comprehensive review of the application of AI in robot-assisted surgery, summarizing three main aspects: medical image processing, surgical planning and navigation, and motion control and decision-making. Leveraging AI technology, the application of medical image processing enables physicians to obtain more precise, higher-definition, and visually intuitive imaging data. It allows for accurate segmentation and alignment of lesions and tissues, as well as automated recognition and analysis of pathological or abnormal areas within medical images. The application of AI in surgical planning and navigation allows surgeons to precisely plan surgical procedures and provide accurate navigation guidance. By integrating personalized patient data and the extensive experience of surgeons, AI assists in predicting surgical risks and provides real-time guidance for precise localization and skillful manipulation during the surgery. Moreover, the application of AI in surgical robot motion control and decision-making enables robots to execute tasks more efficiently and make intelligent decisions. AI algorithms can analyze complex information in the surgical environment in real-time, facilitating precise motion control for the robot. Finally, this research also analyzes the development opportunities and challenges of AI in robot-assisted surgery, offering guidance and insights for future research in the field.

The joints of rehabilitation robots often require good variable stiffness characteristics to adapt to the compliance of the human body. According to the characteristics of the joints and muscles of the upper limbs, the stiffness requirements of different positions in the rehabilitation circular trajectory training of the upper limbs are analyzed, and a new passive variable stiffness driver is designed on this basis. Thereby, the stiffness changes at different positions during the training of the upper limb circular trajectory can be realized. The mathematical model of the passive variable stiffness device is established, and the accuracy of the model and the feasibility of the overall scheme are verified through software simulation and experiments. The passive variable stiffness drive realizes the stiffness change in the upper limb rehabilitation training process. The structure is simple and does not require additional driving and control.

The high-quality welding of aluminum alloy plays a vital role in lightweight manufacturing. However, aluminum alloys are susceptible to solidification cracking and are easy to crack during welding, which seriously affects the quality and safety reliability of welding structures. Solidification crack occurs in the mushy zone behind the high-temperature molten pool. The initiation and propagation of solidification cracking is a high-temperature complex process affected by many factors. The high-temperature metallurgical behavior in the mushy zone is particularly critical. A comprehensive analysis of the research status of welding solidification cracks of aluminum alloys and the crack resistance of filler materials has important guiding significance for deep understanding of the mechanism and suppression of welding solidification cracks of aluminum alloys. The current status and progress of research on solidification cracking mechanism, criterion, susceptibility testing, influencing factors, and cracking resistance of filler materials are summarized. In addition, future research directions are also pointed out. A valuable reference is provided for the in-depth study on the metallurgical mechanism of solidification cracking of aluminum alloy and crack suppression.

The surface quality of monocrystalline SiC, as a semiconductor material, influences its electrical, magnetic, and optical performance. However, during mechanical processing, the cutting depth and the mechanical runout lead to unstable cutting force, resulting in surface micro-burr, modified layer, subsurface residual stress and damage. Thus, an impulse-discharge between wheel metal and SiC is proposed in diamond grinding to drive a loose-abrasive flow. It aims to investigate the mechanism by which the hybrid effects of mechanical processing, abrasive flow polishing, and thermochemical removal influence surface integrity. The surface formation is first modelled in relation to impulse discharge energy and hydrodynamic pressure. Then, the material removal rate and abrasive wear rate are investigated. Finally, the surface integrity is investigated. It is shown that the hybrid machined formation chain was formed in the abrasive-workpiece interface. The impulse-discharge drove the loose-abrasive and modified the interface by thermochemical modification. The loose-abrasive obtained a removal force to eliminate the modified SiO2 and expose the SiC substrate. Decreasing the impulse-discharge energy and hydrodynamic pressure promoted brittle removal to ductile removal. Under the mechanical vibrations generated by large cutting depths and spindle runout, the hybrid machining of impulse-discharge driven thermochemical modification and loose-abrasive polishing, in conjunction with diamond grinding, could effectively reduce residual compressive stress, surface micro-burrs, and subsurface damage layer thickness by 93%, 73%, and 50%, respectively.

In order to meet the key requirements of lightweight and big absorbing bandwidth for the development of wave absorbing materials towards engineering applications, a hierarchical heterogeneous isomeric wave-absorbing metamaterial composing of lossy resonant structures and functionalized dielectric layer is proposed. The lightweight and the broadband absorbing properties of wave-absorbing metamaterial are achieved by bionic structural design for the dielectric layer. A composite material with specific conductivity is prepared by combining thermoplastic polymer material and silver copper alloy. By optimizing the weight ratio of silver copper alloy and the printing process of composite material, the conductivity of the resonant structure is controlled precisely. Based on this, two lossy resonant structure layers are designed to regulate broadband impedance matching and ohmic loss, then the hierarchical isometric heterogeneous wave-absorbing metamaterial is realized based on the dual-material fusion deposition 3D-printing process. The equivalent density of the prepared wave-absorbing metamaterial is 0.3 g/cm³. The electromagnetic performance test showed that the absorbing metamaterial, under the condition of vertical incidence, achieved high-efficient absorbing performance in 3.8-19.24 GHz. At the same time, when the electromagnetic wave is in the modes of transverse electric wave (Transverse Electric, TE) and transverse magnetic wave (Transverse Magnetic, TM) and the incidence angle is in the range of 50°, the wave-absorbing metamaterial had good broadband absorbing performance. The design and fabrication methods for the hierarchical heterogeneous isomeric wave-absorbing metamaterials provide technical support for promoting the development of wave-absorbing metamaterial to engineering application.

Trust is one of the key issues of intelligent connected vehicle human machine interaction(ICV-HMI), which is an important factor affecting the social acceptance, safety and positive user experience of the ICV. Firstly, the concepts related to ICV-HMI trust are defined based on trust research history and the content of ICV-HMI, then, the research framework of ICV-HMI trust is constructed accordingly. Based on the research framework, by analyzing related literature, the current research and application status are summarized in two aspects: trust in the interaction between human and autonomous driving, as well as vehicular networked applications in ICV. Due to the ultimate goal of trust researchin human-machine interaction to calibrate it, the methods are proposed and discussed: constructing trust-influenced mechanisms towards different roles in ICV, measuring dynamic trust during information inputting, and designing ICV-HMI to calibrate trust via information outputting. Accordingtothecalibrationmethods, the key techniques for trust calibration are concluded, those are, the techniques for extracting and characterizing the features of factors shaping trust, the techniques for measuring dynamic trust, the techniques for designing to calibrate trust, and the techniques for assessing the calibration. Ultimately, the future research directions of ICV-HMI trust research are concluded for trust calibration.

When autonomous vehicles operate in off-road environments, they often face complex terrains and constantly changing road conditions. To realize reliable and efficient path planning and ensure the safe and maneuverable operation of the vehicles, a path planning method for off-road autonomous vehicles that takes into account complex terrains is proposed. The method consists of global path planning and trajectory planning. For global path planning, an improved Theta* algorithm based on rough terrain artificial potential fields is proposed. This algorithm considers factors such as slope, ground type, and elevation to keep the vehicle away from rough terrains. By reducing the slope and undulating terrains in the path, the efficiency, comfort, and safety of the vehicle in off-road environments are enhanced. Regarding local trajectory planning, an adaptive probabilistic roadmap method(APRM) algorithm is presented for handling dynamic driving scenarios. It utilizes different sampling strategies to adapt to the changing off-road driving conditions and obstacles. This enhances the efficiency of constructing the path network for complex off-road environments. Experimental verification shows that the improved Theta* algorithm reduces the average slope of the global path by 35.63% and decreases the surface undulation by 33.56%. The APRM algorithm reduces the time for local trajectory planning in unstructured roads and open terrains by 79.68% and 54.74%, respectively.

In the face of my country’s huge demand for construction rebar and the requirements for efficient and precise manufacture of threads at both ends of the rebar, new processing techniques and equipment are urgently needed to meet the requirements. The relationship between the principles, characteristics and process parameters of various thread rolling processes is analyzed, and a new composite process for stripping and rolling of rebar is proposed. Based on this process, a symmetrical rib-stripping rolling equipment is designed and developed. The rolling die phase-maintaining mechanism and the symmetrical mechanism significantly improve the reliability and efficiency of the thread forming of the rebar. The deformation of the main shaft and the mode of the box are analyzed by statics. The plastic deformation and material flow law in the rolling process were analyzed by finite element analysis, and it is found that the equivalent strain of thread forming during the rolling process increases with the feed of the die, and concentrated around the tooth profile in the cutting section, the correction section and the forming section. The segments are distributed over the entire thread, and no stress or strain occurs in the blank core. The flow of flank material from the bottom to the top resulted in a gradual increase in the tooth height, and the axial displacement of the tooth root is the largest. The rib stripping and rolling test is carried out on the developed equipment to verify the feasibility and reliability of the rib stripping rolling process and its equipment. The results show that the increase of blank diameter could eliminate the defect of tooth tip. The die center distance is an important factor to ensure the dimensional accuracy of the formed thread.

The purpose is to figure the lack of interpretability for current industrial intelligence diagnosis methods, review the development situation of model-agnostics attribution analysis in industrial intelligence diagnosis and point out the potential development direction. The main viewpoints and functions of interpretable techniques are analyzed. Aiming at two characteristic problems of industrial intelligence diagnosis, i.e., nonlinear high-dimensional observation and inaccurate knowledge representation, attribution interpretation provides effective methods for understanding forward logical structure and reverse optimizing design of intelligent models. The core concepts, existing works and pros and cons of attention mechanism, saliency analysis, rule extraction, and proxy model are systematically summarized. Four case studies are used to illustrate the result of attribution interpretation techniques. Finally, potential research directions of attribution interpretation technology in industrial intelligent diagnosis are discussed, including quantification of interpretability, feedback to model design, balance between model complexity and interpretability, and attribution analysis in high dimension. Through this review, we hope to provide a suggestion to conduct further development of interpretable intelligence in industrial fault diagnosis.

With the new-generation information technologies and artificial intelligence that are deeply integrated with the industry, the maintenance of industrial systems is moving from the manual regular maintenance to state-based intelligent maintenance(IM). Health status assessment(HSA) is a key link in IM. Systemic states are recognized via the monitored data whereby provide decision support for maintenance. Taking nuclear power plants(NPP) as the research object, an HSA framework is proposed based on multi-level integration of equipment, system, sub-function and function in a multi-department collaboration. Due to equipment groups with numerous state parameters, a weighted average fusion operator based on deviation weighting is proposed to fuse equipment-level parameters, which can timely highlight the abnormal equipment. Considering the different amount of data in different health states, an asymmetric multi-class learning method under imbalanced datasets is proposed to fuse the systems’ health values. The self-learning HSA model is established by the information transfer between multiple health levels, so that the health status of sub-functions can be self-perceived and assessed in a timely manner. The health assessors of multiple sub-functions are fused based on ensemble learning to obtain a macro-level operational function HSA decision. Exemplified with the reactivity function of the NPP, the proposed HAS frame is verified effective.

Carbon fiber reinforced ceramic matrix composites (C_f/SiC composites) have the advantages of good chemical and thermal stability, high specific strength, high temperature resistance and low density. They are widely used in aerospace, high-speed trains and nuclear energy. Currently, ceramic matrix composites (CMCs) components are generally prepared by near net forming technology. However, secondary machining is still required to meet the dimensional accuracy and geometric tolerance of the final assembly. Owing to the characteristics of C_f/SiC composites such as anisotropy and multiphase heterogeneity, the need for efficient and low-damage machining of C_f/SiC composites is concerned. Hence, the research progress of ultrasonic vibration-assisted milling (UVAM) technology for C_f/SiC composites reported in the literature are systematically reviewed. Firstly, the research status of traditional machining and special energy field assisted machining technology of CMCs are summarized. Secondly, the material removal mechanism and ultrasound action mechanism are summarized using the orthogonal cutting tests and finite element simulation during UVAM of CMCs. Then, the modeling method of cutting force of CMCs in UVAM is discussed, and the research on machined surface damage and residual strength of CMCs is introduced. Finally, the development trend of UVAM of CMCs and the outlook on future research directions are analyzed.