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.

In the era of “big data”, artificial intelligence(AI) has emerged as an important approach in the field of industrial intelligent diagnosis, owing to its powerful data mining and learning capability. It plays a significant role in tasks such as anomaly detection, fault diagnosis, and remaining useful life prediction of mechanical equipment. As mechanical equipment continues to evolve towards larger scale, higher speed, integration and automation, the reliability of diagnostic methods has become crucial. Consequently, the lack of interpretability has become a major obstacle to the practical application of AI technology in the field of diagnosis. To promote the development of AI technology in industrial intelligent diagnosis, a comprehensive review of explainable AI(XAI) methods is provided. Firstly, the concept and principles of XAI are introduced, along with a summary of the main perspective and classifications of current XAI techniques. Subsequently, the research status of inherently explainable AI techniques empowered by signal processing priors and physical knowledge prior from industrial diagnosis is summarized. Finally, the challenges and opportunities associated with priori-empowered XAI are highlighted.

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.

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.

To overcome the shortage of elastic elements in rigid leg in traditional bipedal robots, a novel leg scheme with artificial tendon inspired from tendon-muscle complex in human’s leg and foot. A 4-DoF biped prototype with five-linkage configuration is also developed. The optimization paradigm of bipedal walking is constructed based on the linear inverted pendulum (LIP). The dynamical walking controller is devised based on the LIP model embodying the swing and the stance part. In swing, a PD control strategy is employed by combining the Bezier spline-based foot trajectory planning and model-based feedforward compensation. In stance, a control strategy with the feedforward of ground reaction force is proposed by integrating the feedback control of body pitch and height. The effectiveness of the proposed algorithm is experimentally validated. Experimental results demonstrate that the bipedal robot achieves stable walking at 0.8 m/s (almost 2 times of leg length per second), and the fluctuations of the body pitch and height are restrained within ±7° and ±4 cm, respectively. The aforementioned contributions can be further extended to the systematic design of humanoids executing mobile manipulation in 3D world.

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.

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.

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.

Four-wheel independent steering distributed drive electric vehicle has unmatched mobility, traffic ability and handling stability of conventional vehicles, but its complex structural characteristics and high redundancy of actuators increase the risk of steering motors failure. Aiming at the driving stability problem under the failure of single wheel steering, by analyzing the influence of active steering of rear wheels on body stability and using the monorail model with additional rear wheel steering as the steady-state reference model, a hierarchical control method based on tire force distributable reconstruction is proposed. At first, the steering control method of each wheel is determined according to the side deviation state of the tires，considering the steering failure shock and the accumulation of yaw angle error, a recursive non-singular terminal sliding mode controller as the upper level controller is designed based on parameter adaptation. Then, using he minimum tire load rate as the optimization goal, the intervention and withdrawal of the actuator are dynamically adjusted according to the vehicle state, and the wheel steering angles and driving or braking torques are redistributed to design the lower-level controller. Finally, the proposed control method is verified by off-line simulation and controller-in-the-loop simulation. The results show that the designed controller can reduce the maximum lateral position error by 85.2% and 88.1% respectively compared with the common sliding mode controller under the condition of single wheel steering failure, the maximum yaw angle error is reduced by 81.8% and 81.5% respectively, which can keep good driving stability of four-wheel independent steering distributed drive electric vehicle.

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.

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.

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.

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.

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.

Torque converter stator blade angle optimization is an important and economical design solution to improve its performance. Based on computational fluid dynamics, the flow field of the torque converter is simulated numerically and the external characteristics are predicted. The three-dimensional vortex system structure of the stator channel is reconstructed and the two-dimensional cross section velocity field and vorticity field are extracted. Through qualitative and quantitative comparative analysis of the internal/external characteristics, the influence law of the change of the stator blade outlet angle on the performance of the torque converter is revealed. The results show as follows: ① Under low speed ratio conditions, when the stator blade outlet angle is reduced, the torque ratio is basically unchanged, and the torque coefficient of the pump is significantly reduced, especially in the angle variation range of 28°-22°; ② Liquid flow impacts the head of the stator blade to form a cylindrical flow around it. As the stator blade outlet angle decreases, the continuity of the flow around the cylinder is strengthened, and the curvature radius gradually decreases. The blade pressure surface attachment vortex is stretched and deformed, and the blade suction surface long vortex gradually moves toward the middle of the flow channel. When the outlet angle is 20°, it gathers with the attached vortex, and the energy accumulates, causing the jet flow rate at the tail of the blade to increase. The multi-scale vortex carrying energy is torn and collided, and the vortex shedding phenomenon is intensified. ③ Reducing the stator blade outlet angle causes the inlet flow rate of the pump to increase, resulting in the reduction of the pump torque, resulting in the reduction of the pump torque coefficient. Based on the analysis of flow field structure and external characteristic parameters, the influence of stator blade outlet angle on the performance is revealed. The research results can provide some technical guidance for the blade design and performance optimization of torque converter.

Traditional computer-aided design relies on geometric features, quantitative characterization, and trial-and-error. It does not conform to the conceptual design stage with incomplete design information and chaotic design cognition. China is entering a new development period of the 14th Five-Year Plan. The fusion of cognitive science and artificial intelligence brings new opportunities and challenges to the conceptual design of complex equipment. Human-computer cognitive collaboration-driven conceptual design organically combines object-oriented and subject-oriented aspects. It develops from experiential trial-and-error reasoning to conceptual design with explicit cognition of design laws. The inherent connection between the recursive iteration of design objects and the cognitive evolution of design subjects is revealed to render abstract design procedures comprehensible and operational for computers. The state-of-the-art in object-oriented and subject-oriented conceptual design of complex equipment is illustrated firstly. The key technologies such as semantic cognitive identification of customer needs, neuroimaging of thinking cognitive laws, intelligent cognitive reasoning of function-structure mapping, and collaborative cognitive decision-making of concept schemes are analyzed. Through revealing the limitations of existing computer-aided conceptual design methods, a new generation of computer-aided conceptual design based on human-machine cognitive collaboration has prospected.

The current rehabilitation of stroke patients is primarily assisted by rehabilitation physicians. However, in our country, rehabilitation medical resources are not abundant enough to meet the urgent needs for stroke and hemiplegia rehabilitation. Robot-assisted rehabilitation therapy is a new technology that helps stroke patients recover. Upper-limb rehabilitation robots can help patients complete rehabilitation training, regain their motor function, reduce the work intensity of physicians. They have been used in clinical treatments. This article analyzes the physiological structure of the upper limbs of the human body and points out the rehabilitation needs of stroke patients. The upper limb rehabilitation robots are classified according to the interaction mode and the actuating mechanism, and their structural characteristics and application scenarios are described in detail. The typical robot control strategy, stroke magnitude and motor ability evaluation criteria are summarized. Finally, this study analyzes the current challenges and problems faced by upper limb rehabilitation robots, and a discussion on the future directions of research is included. From the perspective of the integration of medicine and engineering, the current state of research on upper limb rehabilitation robots is reviewed, and the technological shortcomings are highlighted, providing research directions for fostering innovation and practice in this field.

Safety of the intended functionality(SOTIF) is one of the biggest challenges in the commercialization of intelligent connected vehicles. In the process of conducting SOTIF analysis on intelligent connected vehicles, establishing appropriate risk acceptance criteria can provide more accurate evaluation criteria for hazard identification and risk assessment, and help reduce development costs, improve the SOTIF confidence and development efficiency of the entire vehicle. In order to establish appropriate risk acceptance criteria, some key parameters of driver traffic characteristics in natural driving data are extracted to validate the “extreme value characteristics” of driver behavior and the normal distribution of parameters. Three basic principles that need to be followed in establishing safety acceptance criteria are analyzed. Based on the practical application of the six standard deviation (6sigma, 6σ) theory in the field of engineering, it is proposed to use the 6σ theory to establish the 3σ acceptance criteria for SOTIF risk assessment. Based on this acceptance criteria, the calculation method of the standard value of the driving safety index(DSI) in the driving safety field(DSF) is redefined. Finally, the TrafficNet database is used to calculate the standard value of DSI(DSI^*) under different basic scenarios, quantifying the SOTIF risk acceptance criteria under the basic scenarios. The research results have improved the SOTIF evaluation system for intelligent connected vehicles. They are of significance for improving the safety level of intelligent connected vehicles fundamentally and avoiding endless accumulation of testing mileage.

Environmental perception and state estimation is one of the key technologies of intelligent network coupling. Simultaneous location and mapping technology(SLAM), which is widely used in the field of intelligent network connected vehicles, aims to complete its own state estimation and environment modeling at the same time. Scholars in the SLAM field are committed to finding a balance between real-time and accuracy of the algorithm. Visual-inertial odometry(VIO), one of the instances of SLAM schema, is favored by most researchers because of its higher performance and lower price. VIO introduces IMU measurement on the basis of visual odometry(VO), which can not only improve the problem of scale drift, but also greatly alleviate the visual positioning failure caused by image overexposure and feature loss in the short term. As a perceptual measurement with good signal-to-noise ratio, the image can extract high-precision multi view geometric constraints, estimate inertial measurement unit(IMU) bias and noise, and eliminate the cumulative error. Thus, VIO not only improves the accuracy by combining redundant sensors, but also ensure the real-time performance of the system through sliding windows and state marginalization, which is a model taking into account both accuracy and operation efficiency. The standard definition and basic model of VIO system are introduced in detail, and its key modules, including initialization, visual information extraction and correlation, solution and optimization and calibration, are combed in detail and reviewed. The advantages and limitations of frontier work are analyzed in detail, and the commonly used visual inertia data sets are summarized. The existing problems and future development direction of vio are summarized and prospected.

Magnetic actuated miniaturized medical robots (MAMMR) can be controlled by an external magnetic field to actively navigate through the narrow cavities of the human body, enabling inspection and treatment of the deep-seated diseased areas. This technology has great potential in the clinical medical field. According to the size difference, MAMMR can be divided into centimetre/millimetre-scale capsule robots, millimetre/micrometre-scale continuum robots, and micro/nanometre-scale microrobots. The magnetic-control principles, structural design and potential medical applications are systematically summarized, and the latest research progresses are reviewed. Finally, the prospects for future research directions of MAMMR are discussed, including the biocompatibility of robot materials, visual feedback for executing medical tasks, miniaturization of multiple medical modules, stability and robustness of motion control.

Trajectory planning is a basic function of autonomous vehicles(AVs). With the development of vehicle-to-everything(V2X) technology, many AVs are equipped with intelligent connected capabilities, and these vehicles are called connected autonomous vehicles(CAVs). The intelligent connected technology can bring much information to AVs, enhance cooperation between different AVs and provide unprecedented opportunities for planning vehicle trajectories to reduce travel time, improve driving comfort and increase safety. Compared to traditional single-vehicle trajectory planning, multi-vehicle trajectory planning can fully utilize the technical advantages of CAVs and plan suitable trajectories for multiple AVs. Typical multi-vehicle trajectory planning application scenarios are overviewed according to structured and unstructured scenarios, and different cooperative planning strategies and characteristics of multi-vehicle trajectory planning are summarized. Various approaches used for multi-vehicle trajectory planning are summarized including traditional pipeline planning methods and end-to-end methods, and experiments on multi-vehicle trajectory planning are generalized. Based on the current research status, the challenges and future research directions of multi-vehicle trajectory planning are presented to provide inspiration and reference for researchers in the field of intelligent transportation systems.

Intelligent electric vehicles require the brake system to realize functions such as active braking and braking energy recovery, and traditional brake systems cannot meet the above requirements. The brake-by-wire system has the advantages of compact structure, rapid response, precise control, and strong compatibility. It is an ideal actuator for autonomous driving and has become a current research hotspot. To systematically and timely grasp the development trend of this field, the key technologies and research progress of the brake-by-wire system for intelligent electric vehicles are reviewed. The types and characteristics of the brake-by-wire system are introduced, and the development trend and research focus of the structural scheme of the brake-by-wire system are clarified. Then the typical products and characteristics of the brake-by-wire system are summarized, and the overall control architecture of the brake-by-wire system for an intelligent connected electric vehicle is proposed. On this basis, the key technologies such as testing and modeling of the brake-by-wire system, power cylinder pressure control, wheel cylinder pressure control, wheel cylinder pressure estimation, solenoid valve control, clamping force control, pedal feel simulation control, sensor fault diagnosis, and personalized control are sorted out. The vehicle's longitudinal motion control methods, such as anti-lock braking, adaptive cruise, and automatic emergency braking based on the brake-by-wire system, are summarized. Finally, the problems faced by the research on the brake-by-wire system for intelligent electric vehicles and the future development trend are analyzed and prospected.

Laser-assisted machining (LAM) is an advanced technique to machine difficult-to-cut materials through laser preheating of local zones, which can reduce cutting forces and improve machining efficiency. In order to obtain the best machinability, one of the most important factors is to control the preheating temperature field. By quantitative and formula characterization of the actual output laser beam quality, an analytical model is adopted to characterize the temperature field induced by the moving laser source. Following, a controlling strategy is proposed for adapting the moving path of laser source to obtain the uniform distribution of temperature field in the cutting area of face-milling, which is then validated against the experimental data by surface and internal temperature. Furthermore, cutting tests under laser-assisted conditions and conventional conditions are performed to analyze the alterations of machinability induced by different machining techniques. Cutting forces and surface roughness are used for the analyses, and the results show that the milling forces can be effectively reduced by more than 10% and the surface roughness can be reduced by 30% with the implementation of the proposed controlling strategy, which further improves the machined surface quality. In summary, the proposed controlling strategy of preheating temperature field can effectively regulate the temperature of heat affected zone, which provides theoretical guidance for the active design of laser parameters, and have broad application prospects in the laser-assisted machining of difficult-to-cut materials.

The thermal deformation during wire electric discharge cutting (WEDM) processing of thin-walled metals is almost unavoidable, which restricts the application of WEDM in precision machining of thin-walled parts. In this study, a new process of underwater laser-induced shockwave to regulate the bending deformation of thin-wall metal induced by WEDM are proposed, which can obtain low/no deformation thin-wall parts. The thermal deformation of In718 and TC4 thin walls processed with different wire-cutting parameters is calibrated, and the relationship between the thermal and wire-cutting process parameters is obtained. The influence of thin walls' thermal deformation is most obviously affected by pulse time, while the effect of pulse gap is not significant. Based on the results of laser shock simulation and experiment results, the thermal deformation of thin walls can be effectively recovered by selecting suitable laser parameters for impact processing of thin walls. For In718 and TC4 thin walls of different thicknesses, the thermal deformation can be controlled by underwater laser-induced shockwave. When the laser energy is 80 mJ, the spot distance (x-y) is 0.5-0.5 mm, and the number of impacts is 1, the control effect achieves the best. The lowest warpage of all thin walls after regulation is only 1.4 μm, and the thermal deformation can be reduced by up to 98.35%. By testing the surface integrity of the processed thin walls, the results show that this new process can improve the surface hardness of the thin wall by more than 20%, reduce the surface roughness and the thickness of the recast layer, and improve the surface cracks to some extent.

Gripper is an important part of the operational robot system, and its operational form and performance determine the overall capability of the robot. Through the innovative design of the gripper can effectively reduce the dependence of the robot on material processing, parts assembly, sensing and control algorithms, and significantly improve the robotics ability to adapt to complex operating environments and objects. The present types of robot grippers are mainly divided into two categories: rigid gripper and flexible gripper. Compared with rigid grippers, flexible grippers have good advantages in unknown environment, gripping geometrically non-regular objects, gripping fragile objects on the surface and safe human-robot interaction. At the same time, the flexible gripper in the realisation of smooth deformation is also accompanied by the existence of a small output and poor operating accuracy due to the lack of structural rigidity, which has not yet been well resolved. Therefore, how to keep the load output capacity of the flexible gripper while providing smooth deformation is a problem that needs to be solved urgently at present. This paper focuses on the common topic of institutional design and variable stiffness technology of flexible gripper, combined with the requirements of enhancing the shape adaptability and operational load capacity of flexible gripper, detailed analyses of the significant progress and shortcomings in the development of flexible gripper from the two aspects of the configuration design and variable stiffness technology. The development technology and the main challenges faced by the flexible gripper are summarised systematically and providing new ideas for the multi-functional and intelligent development of the flexible gripper, expanding the application fields of the flexible gripper and enhancing the overall operation capability of the robot.

Traditional machining methods face great challenges when machining small holes with super depth-diameter ratio on difficult to machine materials. At present, electrical discharge machining (EDM) is still one of the most effective methods for machining deep holes on difficult to machine materials, but there are some problems such as poor surface quality. Electrochemical machining (ECM) has the advantage of high surface quality, but it is not as efficient and accurate as EDM in deep hole machining. Therefore, combining EDM and ECM is the most effective way to achieve high efficiency, high quality and high precision machining of deep holes. This study applies low concentration hydrochloric acid electrolytic-dielectrics to EDM ECM hybrid processes. The experimental results confirmed that this electrolytic-dielectrics was superior to traditional neutral electrolytic-dielectrics in terms of machining efficiency and accuracy. To improve the machining accuracy and surface quality of small holes with super depth-diameter ratio simultaneously, this study proposes a method of partial insulation tool electrode. A high-quality and high-precision machining of small holes with super depth-diameter ratio (> 50:1) through process parameter optimization is obtained. This method has broad application prospects in the processing of small holes with super depth-diameter ratio in difficult to machine materials.

A large-scale parallel topology optimization framework is developed to maximize the eigenfrequency of 3D shell-infill structures. The shell and infill are described through the two-step filtering approach, then the topology optimization model of the shell-infill structure with eigenfrequency objective and mass fraction constraint is built. The parallel solution of filtering equations and generalized eigenvalue equations is based on two software libraries called PETSc and SLEPc. Sensitivities of eigenfrequency and mass with respect to the design variables are derived, which are submitted to the solver of the method of moving asymptotes(MMA) for design variable updating until convergence. During the topology optimization, the modal-assurance-criterion-based(MAC) mode-tracking strategy is employed to handle the mode switching problem. Numerical examples with different mass fraction constraints are investigated to demonstrate the algorithm validity. Finally, the topology optimization design of 3D shell-infill structures with 700, 000 degrees of freedom is realized and the influence of the number of CPU cores on the performance of the algorithm is analyzed, which reduces the computing time by 43.7% with 28 cores compared with 12 cores. The topology optimization method of shell-infill structures is extended to be applicable to eigenfrequency maximization and large-scale parallel computing, which provides an effective idea for lightweight design of complex equipment structures.

The highly nonlinear nature of the contact problem makes it difficult to achieve high material utilization of the assembled structure and uniform stress distribution at the contact interface simultaneously. Most existing topology optimizations that consider the stress distribution at contact interface require relatively complicated processing and large computational resources. To address this issue, the idea of collaborative optimization of assembly structure and material stiffness in contact problem is proposed, and the sequential optimization strategy of topology optimization followed by material stiffness optimization is adopted. That is to say, an assembled structure with high material utilization is obtained through topology optimization first, and then it is used as the input of followed material stiffness optimization to achieve a uniform stress distribution at contact interface through material stiffness optimization. This idea features the direct application of established mature topology optimization methods without complicated processing to deal with the contact nonlinearity. Further, the collaborative optimization model of topology and material stiffness in contact problem is developed, and a Von Mises stress-based collaborative optimization method of topology and material stiffness is proposed. Sensitivity analysis is not required during the optimization process. Finally, the feasibility and effectiveness of the collaborative optimization of structural topology and material stiffness are verified based on a typical elastic-rigid assembled structure and an elastic-elastic assembled structure.

The uncertainties of human-machine interaction would cause conflicts between the driver and the intelligent assisted driving system, and thus deteriorating the vehicle driving performance. To enhance the drivability and lateral stability of the vehicle, an intelligent human-machine cooperative control framework, which considers the dynamic intervention penalty, is proposed. First, to well address the uncertainties in the human-machine shared driving system, the time-varying driver preview behavior and the tire nonlinear characteristics are considered in the vehicle system modelling; Second, to attenuate the conflicts between drivers and assistance steering actions due to the personalized driving behaviors, the human-machine intervention penalty factor is introduced into the driving authority allocation, and the fuzzy rule is established based on the dynamic driver torque and lateral deviation of actual preview point. Third, a linear parameter varying(LPV) controller based on the system poles placement is developed to improve the control system robustness. Finally, to verify the feasibility and effectiveness of the proposed control strategy, the Matlab/Carsim joint simulation and the hardware-in-loop(HIL) test based on NI-LabVIEW-RT system are conducted. The results show that the proposed human-machine cooperative control framework can effectively mitigate the human-machine conflict while guaranteeing the vehicle handling performance.

The separation-contact in ultrasonic vibration milling will affect the high-efficiency and high-quality machining of titanium alloys. To further understand the influence of separation-contact on ultrasonic vibration-assisted milling, the milling models of contact rate and average cut thickness ratio are established, and the discrimination criterion of the ultrasonic action based on the average cut thickness ratio is proposed. By carrying out milling experiments, the mechanism of contact rate and average cut thickness ratio in periodic and continuous contact ultrasonic milling is discussed. The effect of milling and ultrasonic parameters on the tool wear of titanium alloy is investigated. The results show that the separation effect and the average thickness effect can improve the machining quality compared with traditional machining in longitudinal ultrasonic vibration-assisted milling of titanium alloy. In periodic contact milling, the separation effect caused by intermittent cutting of the cutter-workpiece is the main factor in reducing the force and improving the surface quality. In continuous contact milling (contact rate is 1), the average cutting thickness effect caused by the decrease of the average cutting thickness per unit period can reduce the force and improve the surface quality within a certain range. There is a critical cutting speed value of Ti-6Al-4V titanium alloy, which is about 42 m/min. A reasonable combination of spindle speed and ultrasonic amplitude can change the cutting speed to prevent tool wear. This plays an important role in the high-efficiency and high-quality machining and process control of titanium alloys.

The changes in pitch angle of the vehicle body during braking, especially the “braking nodding” phenomenon before absolute stop, are a significant factor for the compromised ride comfort. In this paper, a comfort control strategy based on brake-by-wire system for distributed drive electric vehicles is proposed. Firstly, the braking intention is determined based on driver input and vehicle speed. Then, the braking mode switching is designed based on the driver's braking intention and vehicle parameters. Under non-emergency braking conditions, a predictive model control method is designed for the front and rear axle braking force distribution. In addition, a smooth switching control algorithm with double closed-loop feedback is presented for smoothing the electro-hydraulic compound braking force. Finally, the proposed strategy is evaluated under various brake scenarios in Simulink-Carsim joint simulation. Finally, the proposed strategy is examined under various brake scenarios in Simulink-Carsim joint simulation. The results show that the proposed scheme can significantly reduce the changes in pitch angle and pitch rate during braking under the premise of ensuring braking performance, only resulting in a braking distance increase of up to 20 cm.

Modern complex equipment structures are usually not only in a complex environment of thermal-mechanical coupling, but also subject to inertial forces that cannot be ignored. A high-efficiency solution method for thermoelastic topology optimization considering topology related inertial loads is proposed for this type of problem. First, a thermoelastic topology optimization model considering thermal, mechanical, and inertial loads is constructed to optimize the compliance of the structure under volume constraints; Secondly, a calculation method for inertial loads is provided, and an improved material interpolation penalty model is proposed to address the issue of non-convergence of material distribution in low density areas. Based on this, the sensitivity of structural compliance to topological variables is derived. Finally, the gradient based Method of Moving Asymptotes is used to update the topology design variables. In addition, a thermal coupling topology optimization platform based on MATLAB and ABAQUS has been developed, which can be applied to topology optimization problems of irregular structural design domains in complex engineering. The numerical example results show that the proposed method can effectively avoid the phenomenon of fuzzy structural distribution in low density areas and has good convergence.

In recent years, mobile robots that combine traditional wheeled, legged, and jumping movements have received widespread attention from researchers. Their advantages in unstructured terrain make them have broad application prospects in emergency rescue, field inspections, underground exploration, and other fields. The current research status of new mobile robots such as wheeled jumping robots, wheeled leg jumping robots, and spherical jumping robots are all introduced in detail in the paper, and a comparative analysis also is conducted from their mechanism design and jumping control aspects. In terms of mechanism design, it analyzes the jumping mechanism design characteristics of wheeled, wheeled leg, and spherical jumping robots in recent years, and summarizes their structural design characteristics. In the section of jump control methods, the aerial attitude control methods and landing buffering control methods of jumping mobile robots were reviewed. Finally, from the aspects of structure, energy storage, intelligent control and so on, the future development direction and trend of jumping mobile robot are discussed and prospected.

Trajectory planning is a vital function in vehicular automatic parking systems. Existing algorithms for automatic parking trajectory planning fail to balance generalizability, precision, time efficiency, and solution optimality. Numerical-optimization-based trajectory planning is considered in this work. Initially, the concerned planning task is formulated as a unified optimal control problem. Subsequently, a half-space constraining theory is introduced, together with a reference trajectory and a trust-region constraint modeling method, to simplify the nominal large-scale and nonconvex collision-avoidance constraints as linear inequalities. Finally, the simplified optimal control problem is solved numerically to derive an optimal parking trajectory. We name this proposed planner predefined space rapid optimization (PSRO) method. Extensive simulations indicate that PSRO outperforms prevalent trajectory optimizers such as OBCA and LIOM with respect to success rate, solution quality, and computational speed.

The development of intelligent connected technology has provided great opportunities for the improvement of traffic safety and efficiency. However, the existing research fails to elaborate the driver’s cognitive response mechanism to the environment in the connected environment, and lacks the quantitative analysis of driving patterns in the connected environment. A method for studying the intersection-approaching behavior process and response mechanism is proposed, and the traffic behavior mechanism of drivers in the connected environment is explored. Two driving scenarios are designed in the driving simulator, namely the benchmark traditional environment and the controlled connected environment. In the connected environment, the driver is provided with the traffic light phase and the remaining time of the current phase state. Parameters such as visual interaction information, vehicle kinematics, and driver operating behavior characteristics of 34 drivers are collected. The interaction frequency and cumulative time percentage of the human-machine interface, the first interaction time and response time, and the behavioral characteristics of drivers approaching intersections are analyzed. A driving pattern extraction model based on bayesian non-parametric method combined with text clustering algorithm is established to achieve quantitative description of driving patterns. The results show that there are significant differences in the human-machine interaction characteristics under red and green light phases, and the first interaction time and response time are highly correlated. The connected environment can significantly improve the efficiency of intersection traffic and improve driving behavior. The proposed driving model can effectively describe the six driving patterns of intersection-approaching behaviors, and the connected environment can reduce the acceleration behavior by 23.7%, and increase the smooth driving ratio by 25.0%.

Grinding is an indispensable method to obtain high surface quality and machining precision of difficult-to-machine materials in aerospace field. Especially for high efficiency grinding characterized by large contact length has been widely used in aerospace field. However, the lubricant traction energy is insufficient in workpiece/abrasive interface, which leads to poor cooling lubrication and infiltration performance, and deterioration of workpiece surface integrity. Based on this, a new magnetic traction nanolubricant grinding is proposed. However, the micro-interface transport mechanics of magnetic nanolubricant under the magnetic field is not clear, and the grinding force model with the influence of magnetic field has not been established. Firstly, the mechanical law of magnetic traction lubricant transport in grinding zone is revealed, and the model of lubricant infiltration velocity and flow rate with the influence of magnetic field is established. Secondly, a grinding wheel model is established based on the truncated hexahedron abrasive, which revealed the interference mechanical behavior of materials for a single abrasive and established the mechanical model. Finally, a grinding force model of magnetic traction nanolubricant is established for titanium alloy grinding and verified by experiments. The results show that the introduction of magnetic field can significantly increase the wetting speed and flow rate of lubricant. When the magnetic field intensity is 5×10⁵ A/m, the normal and tangential grinding forces decrease by 31.8% and 74.3%, respectively, compared with no magnetic field. And the minimum mean deviation of tangential and normal grinding force is 9.2% and 5.7%, respective. A theoretical basis for magnetic traction nanolubricant grinding and technical support for high surface integrity requirements of large contact length grinding of difficult materials were provided.

High-quality and efficient cutting of titanium alloys has always been a difficult problem in the field of machining. Although various types of thermal field assisted high-efficiency precision machining methods are applied, there was a lack of theoretical research on the coupling of multi-energy fields such as its mechanical behavior and cutting temperature, which made it difficult to regulate and optimize the process reasonably and efficiently. To address the above problems, A regulation optimization study based on the multi-energy field model for EDAM with thermal-force coupling is carried out, thus to improve the machining efficiency and quality of titanium alloys. Firstly, capacitance, speed and electrode width angle are determined as optimization variables. Then, the models of discharge-assisted temperature and cutting force for EDAM based on thermal-force coupling are developed. Meanwhile, the influence of optimized variables on the processing characteristics of EDAM is analyzed based on response surface methodology (RSM) to determine the optimal parameter combinations. Finally, the accuracy of the theoretical model and the validity of the optimization results are verified by a series of EDAM experiments of titanium alloys. The results show that through the optimization of the theoretical model to regulate the multi-energy field during EDAM machining, the cutting force and surface roughness of EDAM are reduced by 52.7% and 80%, respectively, compared to conventional milling (CM).

To meet the needs of higher load, efficiency and reliability axial turbine, the blades are designed longer. Compared with shorter blades, the profiles of long blades are torsional and flow parameters are different along the blade height, and its design is more difficult relatively. The long blade stage always operates under off-design conditions, so the stage has complex flow structure and its performance deteriorates, which easily leads to the decline of blade safety and service life. In order to facilitate scholars to further study the characteristics of long blade stage under off-design conditions，and further explore the methods of broadening the operational condition range of long blade stage, a series of relevant literatures are surveyed. Firstly, the aerodynamic and structure design characteristic of long blade are introduced. Secondly, the aerodynamics characteristic, heat transfer as well as strain characteristics of long-blade stage under off-design conditions are analyzed and summarized，besides the influence of exhaust hood on last long blade stage under variable conditions is introduced. Finally, the existing deficiencies are introduced based on the latest research progress, and the future research focus and development trend of axial turbine long-blade are discussed.

How to accurately estimate the state of charge is one of the key technologies for safe application in lithium-ion batteries. However, the current approaches are not fully fit for lithium-ion battery systems, and there needs improvement in accuracy, stability and practicality. In order to describe the dynamic characteristics of lithium-ion battery systems and improve the accuracy and stability, an adaptive extended Kalman particle filter is proposed based on fractional order battery model to estimate the state of charge of lithium-ion. In the process of parameter identification of fractional order battery model with immune genetic algorithm, the "memory vault" is used to reduce the calculation amount of the algorithm, and the "affinity degree" is introduced to solve the problem of local convergence of the algorithm. The proposed control algorithm was downloaded into the battery management system controller by means of model-based development and compared and verified by ECE and UDDS condition tests. The terminal voltage error of the second-order fractional battery model is not more than 13.96 mV, and the average error is 2.4-4.2 mV, which indicates that the fractional battery model is more sensitive to the change of current, and can better represent the performance of the battery voltage change, and can effectively ensure the calculation accuracy of the battery SOC. Compared with the EKF, the accuracy of SOC estimation is improved by more than 50%, and the convergence time is greatly reduced, which indicates that the introduction of adaptive Kalman filter in particle filter for correction can filter out noise and enhance the accuracy and robustness.