Micro-texture is widely used in friction and wear,thermal energy exchange,sealing of key components,biomedicine and many other fields. As a kind of micro-texture,cylinder texture plays an important role in increasing friction.Taking advantage of high electrochemical machining efficiency and no residual stress,a dry film mask electrochemical machining method of group cylinder texture was proposed.MS series dry film with high adhesion was selected to prepare mask,and the multi-physical field coupling analysis of flow field,electric field and reactant transfer was carried out. The effects of different template thickness on the electrolyte flow velocity,bubble rate and conductivity in the processing area were studied. The analysis results show that the template with 50 μm thickness can bring higher electrolyte flow velocity on the surface of the workpiece,accelerate the discharge of products and bubbles,and electrical conductivity of the workpiece surface is higher. The effects of processing voltage,processing time and pulse duty cycle on the size of group cylinder texture were studied by experiments.The experiment results show that under the processing voltage of 25 V and the processing time of 15 s,the group cylinder texture with good localization can be obtained,and moreover,the mass transfer process of insoluble electrolysis products,bubbles,and electrolyte is further improved under the pulse electrolysis processing with a pulse duty cycle of 20 % and a pulse frequency of 4 kHz. Finally,under the preferred processing parameters of 25 V processing voltage,15 s processing time,20 % pulse duty cycle,and 4 kHz pulse frequency,a group cylinder texture array with a diameter of 320.48 μm,a height of 15.06 μm,and a lateral corrosion coefficient EF=1 was processed on the TC4 surface.

On the basis of considering the difference of workers′ skill learning ability,in order to solve the scheduling problem of multi-worker cooperative flexible workshop,a scheduling method based on sparse neighborhood Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) was proposed. The multi-person cooperative flexible job shop scheduling problem under the premise of considering the difference of learning ability was described. The DeJong learning model was improved,and the multi-objective optimization model of multi-person cooperative flexible job shop scheduling was established. On the basis of NSGA-Ⅱ algorithm,the selection method of neighborhood sparsity was introduced,which effectively retained the chromosomes with rich information and diversity,and then the sparse neighborhood NSGA-Ⅱ was applied to solve the scheduling problem. The experimental results show that the Pareto solution set quality of the sparse neighborhood NSGA-Ⅱ is higher than that of the standard NSGA-Ⅱ and the adaptive Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D), and the time of the shortest completion time scheduling scheme is 127.1 min,which meets the constraints of logic and time. The experimental results verify the superiority of sparse neighborhood NSGA-Ⅱ in flexible job shop scheduling.

Fusion Deposition Molding(FDM) is one of the widely used 3D printing technologies currently. FDM 3D printing generating waste materials during the application process. These waste materials are mainly distributed sporadically in the user side and are difficult to collect centrally. What′s more,there is no simple waste recycling and regeneration device,resulting in a large amount of waste being discarded or directly incinerated,which causing resource waste and environmental pollution. Based on the concepts of energy conservation,environmental protection,and waste recycling,a 3D printing waste recycling and regeneration device was designed and manufactured to achieve the heating,melting,and extrusion of waste materials,which resulting in new consumables suitable for 3D printing. This has enabled the recycling and reuse of FDM 3D printing waste,helping to solve problems such as resource waste and environmental pollution. The operation and regeneration experiments were conducted on the device to verify the rationality of the design scheme and the regeneration capacity of the device. The control variable method was used to conduct regeneration experiments on five materials including ABS,PLA,PP,PE and PS,the influence of main operating parameters such as heating temperature and motor speed on the forming quality of recycled materials was obtained. The optimal operating parameters for the five materials were also obtained.

Currently,the mainstream method for direct-writing multi-material 3D printing involves designing a nozzle with multiple inlets to converge into a single outlet,where materials are extruded and organically bond together to achieve a combination of different properties. However,multi-material additive manufacturing requires excellent bonding strength between materials,and the material parameters must meet the requirements of high viscosity and shear-thinning characteristics for 3D printing.In order to achieve continuous and stable printing of multiple materials,the principle of multi-material coaxial printing and experimental printing using a Coaxial Core Multi-material 3D printing (CCM3D) nozzle with a core material that has a rapid switching function was studied. The CCM3D nozzle allows for the stable selective extrusion of core materials with different rheological properties,enabling multi-material coaxial manufacturing. By generating multi-material G-code for printing,the rapid switching between two different viscosity core materials and the printing of 2D patterns are demonstrated. Taking advantage of the benefits of coaxial multi-material printing,silicone with different elastic moduli is selectively embedded into the coaxial core,showcasing its capability to print 3D models.

Orthopedic emergency treatment is highly sensitive to the manufacturing time of braces.3D printing technology provides favorable conditions for rapid manufacturing of personalized braces.Due to the curved tube shape of the elbow support having a large number of suspended structures relative to the printing platform,the supported printing greatly affects the forming efficiency. The five-axis unsupported 3D printing process with rotating bottom plate provides an effective solution for efficient printing. However,the position and pose of the irregular elbow model after field 3D scanning should be adapted to the printing mechanism to print smoothly.In order to solve the optimal pose adaptation problem of elbow model,an automatic pose adjustment method based on 3D elbow model features and printing equipment structure constraints was proposed. Aiming at the irregularity of the 3D scanning model,the spatial pose characteristics of the model were obtained by extracting the center line of the elbow model,calculating the angle between the three axes,and identifying the long and short axes. In view of the limitation of the rotation angle of the bottom plate of the printing equipment and the coordinate position of the growth point of the bottom model,the multi-angle pose of the elbow model was optimized and adjusted,such as the parallel bottom surface,the spatial orientation and the close bottom plate,so as to meet the requirements of 3D printing. The effectiveness of the pose adjustment algorithm for the raised elbow model was verified through simulation comparative analysis and printing experiments,providing support for the slice processing of unsupported fast 3D printing.

Intelligent manufacturing has become the core driving force to promote the industry 4.0 revolution.With the continuous development of information technology,intelligent manufacturing not only requires the automation and intelligence of the production process itself,but also puts forward extremely high requirements for the flexibility,scalability and experimentation of the system. Multi-Agent System (MAS) has great potential in intelligent manufacturing because of its excellent characteristics of autonomy and interaction. However,the traditional development mode of MAS usually has objective problems such as fixed topology,slow response speed and difficult experimental verification,which limits its further development. To solve this problem,a rapid prototyping development system based on Networked Multi-Agent System (NMAS) with switchable topology was proposed to help developers easily obtain the rapid prototypes of target NMASs. The rapid prototype system can realize the flexible switch of topology structure,so as to adapt to the needs of different production scenarios. Through the introduction of advanced communication protocols and cooperative algorithms,the efficient communication,rapid response and user interaction of the development system can be guaranteed,so as to realize the rapid verification and optimization of the prototype development system. Combined with intelligent manufacturing,the system can not only improve the flexibility and scalability of the production process,but also greatly shorten the product development cycle,reduce the generation cost and labor time.

In the multi-variety small-batch discrete manufacturing process, there are multi-source heterogeneous process information such as process drawings, workshop panel records, machine tool sensor signals, quality inspection reports, etc. How to efficiently obtain these data from different sources and diverse structures, effectively realize data visualization, and tap the potential value of data is a common problem faced by this kind of enterprises. In the process of traditional paper-based process information management, there are some disadvantages, such as low informatization degree, poor sharing, not real-time acquisition and weak security. In order to solve this problem, it takes gear, a typical multi-variety and small-batch discrete processing product, as the object, proposes a multi-heterogeneous data integration method that can comprehensively use image recognition, text extraction, two-dimensional code technology, TCP/IP communication and other technologies, and designs and develops an intelligent operation and maintenance system that can support the integration of multi-source heterogeneous data such as text, image and sensor data. It realizes real-time integration of multi-source heterogeneous data acquisition, lightweight and efficient management, value mining and visual application of key gear processes.

To solve the problems of low robot inspection efficiency and lack of obstacle avoidance ability in current power system,an Improved Marine Predator Algorithm (IMPA) for obstacle avoidance was proposed. Firstly,the spiral complex path search strategy based on the Archimedean spiral curve was used to expand the global search range,thus improving the stability of the algorithm. Secondly,the nonlinear convex decreasing weight was introduced to balance the global and local ability of the algorithm,so as to realize the dynamic update of predator and prey population position. In addition,the golden sinusoidal algorithm was combined to update the location of prey,so as to reduce the search range of predator population,and improve the precision and speed of convergence. Finally,experiments were carried out in the distribution room and substation environment. The results show that compared with the basic MPA,the improved ant colony algorithm and the hybrid MPA,the IMPA is more dominant in the travel distance and time,it is more conducive to improve the robotic autonomous obstacle avoidance ability and operational efficiency.

A conical helical microrobot was designed to simulate the execution of complex operations such as targeted drug delivery and vascularization in living organisms. The structure of the conical helical microrobot consists of a cylindrical head,a conical helical tail,and a NdFeB permanent magnet. In order to meet the three-dimensional motion control requirements of the conical helical microrobots in low Reynolds number liquid,a universal rotating magnetic field generator was designed based on the principle of superposition of space vectors,and the magnetic field strength and uniformity of the universal rotating magnetic field generator were optimized by the finite element analysis method. A conical helical microrobot was fabricated and a magnetic drive system was constructed to perform the conical helical microrobot three-dimensional motion control experiments. The experimental results show that the maximum swimming velocity is 1 mm/s for conical helical microrobot in glycerol,which is able to realize stable two-dimensional and three-dimensional motion control as well as complete the complex operation of traversing the maze.

The traditional manual inspection of bridge cables has drawbacks such as low detection efficiency and accuracy,as well as safety concerns. The existing wheeled cable inspection robots till limited by pay load capacity,obstacle-crossing ability,light weight structure,and easy disassembly and assembly,which obstruction its current application capabilities and on-site operational efficiency improvements. A wheeled robot equipped with an enhanced driving system was proposed which comprising four U-shaped wheels and dual engines,enabling to conduct cable surface defect detection under complex conditions. Additionally,models were established to describe the robot′s normal climbing and obstacle-crossing behaviors. The climbing ability of the robot was evaluated through in door and field trials under various conditions. Finally,with the specially designed driving system,the robot can carry pay load of 6.8 kg and driving wheel overcome obstacles up to 6.3 mm in height,and perform well in on-site applications on three bridges,can climb on complex cable surfaces such as inclined,dent,protrusion,and storm line. Further more,field applications on real bridges strongly supported the effectiveness of the proposed enhanced driving system.

In view of problems such as difficulty in path planning of logistics handling robots in complex environments, a trajectory optimization control method for handling robots based on membrane computing and model prediction control was proposed. Firstly, an improved robot dynamic window algorithm based on membrane computing particle swarm optimization was proposed to address the problems of low efficiency in trajectory planning caused by the traditional dynamic window algorithm using uniform and equal division sampling in the speed sampling space. With the help of the randomness of particle swarm optimization and the distributed parallel computing capability of membrane computing, traditional dynamic window algorithms were optimized and iteratively optimized to obtain the optimal path. Secondly, in response to the nonlinear characteristics of the robot system model, a model predictive control method was designed to complete trajectory tracking, by building prediction models, setting objective functions, and eliminating cumulative errors through integration. The experimental results showed that the improved algorithm reduced the average path length, time, and number of steps by 15.07 %, 6.72 %, and 7.68 % in both sparse and complex obstacle scenarios,the proposed model predictive control method has certain advantages in tracking accuracy and system robustness.

Generally,vehicle simulation is studied under the assumption that the subframe is rigid.However,in reality,the subframe is flexible.In order to study the influence of subframe flexibility and material property change on the handling stability optimization,HYPERMESH software modal calculation was used to obtain the flexible front subframe file,and modal analysis was carried out to avoid its resonance with the engine and body. The flexible front subframe was imported into ADAMS software to replace the original rigid front subframe,and a rigid-flexible coupling model of a car with different material properties of the flexible front subframe was established,and the K & C simulation of the suspension and the simulation of the handling stability of the whole vehicle were carried out for comparative study. The NSGA-Ⅱ algorithm was used to optimize the design of the front subframe bushing stiffness parameters with the optimization objective of vehicle handling stability. The results show that the elastic deformation after changing the material properties of the flexible front subframe affects the force on the suspension system,which changes the K & C characteristics of the suspension,and then changes the handling stability of the rigid-flexible coupling model of the whole vehicle. Changing the material properties also causes the results of the multi-objective optimization of the bushing stiffness parameters at the articulations of the front subframe with the suspension and the body to change. After the multi-objective optimization,the yaw rate gain,the body roll angle gain,and the delay time of lateral acceleration are all reduced,and the handling stability of the whole vehicle rigid-flexible coupling model is improved.

To address the issue that traditional vehicle suspension control systems struggle to accommodate multiple influencing parameters, a multi-condition feedback adjustment-based graded control strategy for vehicle suspension is proposed. This strategy introduces three factors affecting comprehensive vehicle suspension performance-spring mass acceleration, unsprung mass acceleration, and road surface excitation-into the traditional PID control system. By employing multiple controllers to form several control loops, spring mass acceleration is used as the primary control factor for direct adjustment of vehicle suspension performance, while unsprung mass acceleration and road surface excitation serve as control factors for two auxiliary adjustment loops, which adjust the PID controller parameters in the primary control loop. This allows simultaneous feedback control from multiple control loops, making full use of various factors affecting vehicle suspension performance and keeping the PID controller parameters within an optimal range. Simulation results show that, on a single bump road surface, the time for spring mass acceleration to decay to 0 in the optimized control system is 0.8 s, which is 77.1 % and 60.0 % shorter compared to passive suspension and PID control, respectively. On a sinusoidal wave road surface, spring mass acceleration is reduced by 58.3 % and 35.1 % compared to passive suspension and PID control, respectively. Additionally, on random graded road surfaces, spring mass acceleration, dynamic deflection, and dynamic load are significantly improved, enhancing vehicle ride comfort and driving safety.

316L stainless steel is used as an important material for making human implants.After researching and proposing a machining method called Nano Hydroxyapatite Powder Mixed Electrical Discharge Machining milling (NHAP-PMEDM-milling) is proposed to explore the trends and differences between two machining processes,NHAP-PMEDM-milling and Nano Hydroxyapatite Powder Mixed Electrical Discharge Machining(NHAP-PMEDM),to etch 316L stainless steel through the variation of factors and levels. The experimental results found that NHAP-PMEDM-milling can improve the material removal rate and reduce the electrode loss,surface roughness and contact angle. It proves that this machining method has research significance,and further explores the different machining characteristics and formation reasons presented under the positive and negative polarity of this machining method through the one-way test. The results of the polarity experiment showed that the positive polarity machining could realize higher material removal rate,while the negative polarity machining presented lower electrode loss rate,surface roughness and contact angle.

Aiming at the problems that the complex failure state of retir parts and the different capabilities of equipments make it difficult to make decisions on remanufacturing process programs,a decision-making method for remanufacturing process programs considering time uncertainty was proposed. Analyzing the uncertainty of the remanufacturing process of retired parts and its time,a fuzzy neural network prediction method was proposed to improve the time of remanufacturing process considering the failure state;a decision-making model of remanufacturing process program was constructed with the optimization objectives of time,cost and energy consumption,and a genetic-particle swarm optimization algorithm was adopted for rapid solution;the effectiveness and practicability of the method were verified by taking the decision-making process of remanufacturing process program of retired rolls as an example.

The monocular structured light system composed of camera and projector plays a crucial role in the field of three-dimensional measurement. System calibration is a pivotal step in ensuring the accuracy of three-dimensional measurements. Traditional calibration algorithms,during the image acquisition process,have not considered the impact of different poses on the precision of system calibration. Addressing the calibration issue of a monocular structured light system with a planar target,two situations that should be avoided during calibration pose selection were proposed. Firstly,poses where the target plane is parallel to the imaging plane of the camera or the projection plane of the projector; secondly,poses involving only translation or rotation within the plane of the target.Building upon this,a criterion for target image selection was proposed,incorporating a threshold for the Euler angles of the target images. This criterion filters out images that negatively impact calibration precision,retaining those with advantageous target poses.Experimental results demonstrate that the selection of target image filtering contributes to ensuring system measurement accuracy,achieving high calibration precision,and meeting the accuracy requirements of contemporary industrial production and processing.

Accurate detection of welding deviations is a prerequisite for automatic seam tracking and intelligent welding by welding robots.An improved ViT-based method for molten pool recognition and online detection of welding deviation was proposed. Firstly,the lightweight ViT model Segformer was used as the baseline model. The Shuffle Attention (SA) was embedded before mask segmentation to better capture the dependencies of feature information in both spatial and channel dimensions. Thus,the model's segmentation accuracy was enhanced.Secondly,a Context Broadcasting (CB) module was added to the Multilayer Perceptron (MLP) to improve the generalization capability while ensuring low parameters of model. Finally,based on the model segmentation results,a welding deviation calculation method was proposed to quantitatively describe the deviation detection accuracy. The experimental results show that,compared with the baseline model,the mean intersection over union and mean pixel accuracy of proposed model were increased by 2.67 % and 2.12 %,respectively,and it has good generalization for different preset torch offsets. The welding deviation accuracy was controlled between ±0.021 mm,which provided a basis for seam tracking in precision welding.

To overcome the influence of uncertain torque such as unknown load,mechanical friction and external interference on the speed control accuracy of position sensorless permanent magnet synchronous motors,a back-stepping hyperspiral sliding mode control method was proposed. Firstly,the conversion relationship between the three-phase coordinate system,two-phase rotating coordinate system and two-phase stationary coordinate system of the permanent magnet synchronous motor was analyzed,and a motion mathematical model of the permanent magnet synchronous motor was established. Then,a back-stepping control law was designed for the speed loop to stably track the speed,and the uncertain torque was estimated by the the designed observer in real-time. The compensation current was introduced to suppress the disturbance of uncertain torque to the control system.Finally,a hyperspiral sliding mode control law was designed for the current loop to achieve high-precision control of the motor speed. The simulation results show that the designed observer can accurately estimate the uncertain torque,and the maximum estimated error is only 0.01 N·m. The proposed control law can effectively overcome the influence of uncertain torque and maintain high accuracy of the permanent magnet synchronous motor speed,and the maximum error is only 0.46 r/min,greatly improving the dynamic control performance of the permanent magnet synchronous motor.The test results show that the designed back-stepping hyperspiral sliding mode control law has higher control accuracy,and the RMSE,MAE and MAPE are only 0.317 7 r/min,0.252 5 r/min and 0.03 %,respectively,which exhibits stronger robustness and better engineering applicability.

The rapid development of new manufacturing modes such as intelligent manufacturing and network manufacturing has brought new opportunities and challenges to the promotion of distributed production mode. At present, domestic and foreign scholars have summarized and analyzed the problems of flexible job-shop scheduling and distributed job-shop scheduling, but there is still a lack of overview of distributed flow shop scheduling. Taking distributed flow shop as the research object, it summarizes the existing research results from the aspects of operation mode analysis, optimization target design, special constraint characterization, model solving methods and engineering applications, and prospects the future research direction of distributed flow shop scheduling.