Process parameters are the key factors affecting the forming quality, performance, efficiency and cost of parts. The intelligent optimization design of process parameters is the basic task of current intelligent manufacturing. According to the analysis of the research hotspots and literature on intelligent optimization design of process parameters in recent years, the method of intelligent optimization design of process parameters is divided into three categories, including intelligent optimization design based on neural network, explicit mathematical model and intelligent algorithms, and expert system (knowledge), Then, the research progress and features of the three methods are analyzed, their advantages and disadvantages are summarized and compared, and the development trend of intelligent optimization design of process parameters in the future is proposed. It can provide a basis and direction guidance for the research on intelligent optimization of process parameters.

Surface defect detection is a key part of product quality inspection, and with the rapid development of deep learning technology in recent years, the surface defect detection technology of metal materials has been greatly improved. This paper compares and analyzes the surface defect detection methods of metal materials based on deep learning in recent years, and discusses the status of research and application effects in recent years from three aspects: supervised methods, unsupervised methods, and weakly supervised methods. Finally, the key problems and solutions in the detection of surface defects in metal materials are systematically summarized. The further development of surface defect detection is considered and foreseen in the light of industrial needs.

Launch vehicles are the main means for a country to enter space, and its manufacturing level is crucial to the process of controlling space. As the main part of the launch vehicle, the structure of the rocket body is the key to rocket manufacturing. After decades of development, the rocket body manufacturing technology has gradually shifted from a manual operation-based model to a green and automated technology-based model. According to the manufacturing situation of the rocket body structure at home and abroad, this article gives a detailed introduction to the key technologies and equipment in the rocket body structure manufacturing process, including sheet metal forming, milling processing, drilling and riveting, welding and rocket body docking. The main process summarizes the domestic and foreign gaps and application difficulties of related technologies, and provides a reference for the development of my country's launch vehicle rocket body structure manufacturing level.

For the multi-objective green flexible job shop scheduling problem, a multi-objective optimization model with minimizing the maximum completion time, total load and total energy consumption as objectives is established, and an improved NSGA-II multi-objective optimization algorithm with adaptive crossover mutation operator and learning mechanism is proposed. In this algorithm, the initial population is obtained by the non-dominated sorting selection strategy based on global, local and random selection through a two-level coding mechanism of machine and process. Hybrid crossover mutation strategy with adaptive operator is adopted to improve the global search performance of the algorithm. A distribution function is introduced to improve the elite preserving strategy and the diversity of population. Neighborhood search is carried out by learning mechanism to improve the local search capability of the algorithm. Finally, Brandimarte and Kacem data sets are used to test the algorithm. The results show that the improved NSGA-II algorithm for solving multi-objective green flexible job-shop scheduling problems has the advantages of high precision, fast convergence and good diversity of solution sets, which can guide the practical production decisions.

Aiming at the optimization problem of minimizing the maximum completion time in job shop scheduling, a deep reinforcement learning optimization algorithm is proposed. First, a deep reinforcement learning scheduling environment is built based on the disjunctive graph model, and three channels of state characteristics are established. The action space consists of 20 designed combination scheduling rules. The reward function is designed based on the proportional relationship between the total work of the scheduled operation and the current maximum completion time. The deep convolutional neural network is used to construct action network and target network, and the state features are used as inputs to output the Q value of each action. Then, the action is selected by using the action validity exploration and exploitation strategy. Finally, the immediate reward is calculated and the scheduling environment is updated. Experiments are carried out using benchmark instances to verify the algorithm. The results show that it can balance solution quality and computation time effectively, and the trained agent has good generalization ability to the scheduling problem in the non-zero initial state.

This paper firstly expounds the related concepts of machine learning and knowledge map, and their applications in the construction of knowledge base and the positions in the industry. Then introduces the common model of machine learning combined with the typical algorithm. To increase the relevance of industry knowledge in the knowledge base and reduce the redundancy, this paper introduces a new technology related industry knowledge map and its construction method, thus led to the study of the method of building knowledge base for industry, and combined with intelligent knowledge base shows the innovative application of knowledge map, using the knowledge map to provide technical support for the search and recommendation feature of the knowledge base, at the same time through knowledge map shown more visually for the domain knowledge. Finally, this paper further describe the role of machine learning and knowledge map play in industry knowledge.

Advances in construction machinery technology are driving an increasing demand for the high-performance bearings. Due to the existence of problems such as clamping deformation, cutting deformation and thermal deformation during bearing machining, the accuracy of finished bearings is seriously restricted, so the deformation control technology during bearing machining has become the focus of research by various manufacturers and institutions. This paper aims to present a comprehensive overview of the current research progress in bearing machining deformation control, analyzes the causes of bearing machining deformation (including cutting force, cutting temperature, cutting tools and other factors). The paper provides a detailed discussion of prevalent methods for controlling bearing machining deformation, such as changing the machining method, changing the structure of the chuck and machining parameters control, etc. Finally, this paper analyzes the advantages and disadvantages of the various bearing processing deformation control methods currently employed. The future trends in research on bearing processing deformation control are also discussed. Given that researching bearing machining deformation control is an important direction in the machining field, this paper will have a positive impact on improving the quality and efficiency of bearing machining.

In order to realize the multiple-objective joint optimization of flexible workshop, such as completion time, machine load, delivery delay time and workshop energy consumption, a multi-objective scheduling method for flexible workshop based on adaptive penalty MOEA/D is proposed. The flexible workshop scheduling problem with multiple production machines, multiple processing tasks and multiple processes is described, and an optimization model is established. A flexible job shop scheduling method based on MOEA/D algorithm is proposed. Aiming at the problem that constant penalty factors cannot meet the different adjustment requirements of different neighborhoods for convergence and chromosome diversity, a penalty factor that can adjust adaptively with the density of adjacent chromosomes is proposed, and a flexible workshop scheduling process based on adaptive penalty MOEA/D algorithm is formulated. In the production scheduling experiment with 8 machine tools and 8 workpieces with 28 processes, the Pareto frontier solution searched by the adaptive MOEA/D algorithm can dominate that of the standard MOEA/D and the improved NSGA-II algorithm; In the production experiment of the equal weight optimal solution, the completion time, machine load, delivery delay time and workshop energy consumption of the adaptive MOEA/D algorithm scheduling scheme are less than those of the standard MOEA/D algorithm and the improved NSGA-II algorithm. The experimental results show that the adaptive penalty MOEA/D algorithm is effective and superior in flexible workshop scheduling.

Intelligent workshop heterogeneous manufacturing resources and workshop disturbance are complex. Moreover，production service modules are more diverse and complex, so in order to realize the clear management of different functional modules in the workshop，design and development of an intelligent workshop production management system based on disturbance events is particularly important. According to users’ requirements for uniqueness and timeliness of products, the business flow and overall architecture of intelligent workshop scheduling management system based on disturbance events were proposed. Using B/S(Browser/Server) architecture, and based on VisualStudio.NET integration platform, using WebASP.NET database development, five functional modules to realize the full use of workshop manufacturing data information were designed, and the main functional interface of the system was displayed.The feasibility and rationality of intelligent manufacturing workshop monitoring and processing abnormal event and production scheduling model are verified.

The dimensional and geometric accuracy and surface quality of complex parts produced by additive manufacturing require secondary processing because they do not meet the requirements for direct application,which restricts the further development of metal additive manufacturing technology in the aerospace industry and other fields, and additive-reduction hybrid manufacturing is the most effective solution to break through the technical bottleneck. Firstly, the principle of hybrid additive and subtractive manufacturing technology is explained, the research status of domestic and foreign additive and subtractive materials is reviewed in terms of equipment integration and process research, the process parameters and defect detection of hybrid additive and subtractive manufacturing are introduced, the key technical difficulties of hybrid additive and subtractive manufacturing are pointed out and the direction of development is indicated.

This paper focuses on the multi-objective flexible job-shop scheduling problem. Because in the actual production process, the scheduling results from completion time, the machine load, cost control, resource consumption and other factors influence, so this paper puts forward a kind of improved genetic algorithm based on multi-objective optimization, to minimize the maximum completion time, minimize the machine load and minimize resource consumption three objective function optimization, combined with the improved Pareto multi-objective optimization method, the shortest processing time variation method and the neighborhood variation method, the optimization ability of the algorithm is improved. Finally, the experimental results show that the proposed algorithm is suitable for solving multi-objective flexible job-shop scheduling problem.

As a key enabling technology to promote the development of intelligent manufacturing, digital twin is drawing great attention on applying in the field of CNC machine tool. However, the lack of general and fundamental standards such as technical terms, reference architecture, key technology leads to problems of application in the field of CNC machine tool. Therefore, digital twin standards of CNC machine tool are urgently required. This study analyses the demand of digital twin standards of CNC machine tool. Based on the above research, combined with five-dimension on digital twin model and research on digital twin technology, this study constructs the framework of digital twin standard system of CNC machine tool, which contains fundamental standards such as general requirements, safety, test methods and key technology standards such as CNC machine tool configuration, data processing, connection and interaction. Discussion is done on the application and function of digital twin standards of CNC machine tool. Hope this work can not only give reference for researchers of digital twin standards and CNC machine tool standards, but also guide for digital twin application in the field of CNC machine tool.

In order to address the welding quality issues caused by inconsistent indoor and on-site construction environment during the hot melt welding of polyethylene pipes, the finite element method in Ansys software was employed to analyze the influence of different environment on temperature uniformity and melt layer thickness, and to evaluate the welding process. Based on the melt layer thickness, an optimization model for the welding process was established and the optimized welding process parameters were validated through tensile experiments. The results showed that under certain environmental conditions, the pipe temperature gradually approached the ambient temperature as the axial distance increased. The uniformity of temperature at the welding end decreased first and then gradually increased with an increase in cooling time, reaching a minimum at around 200 seconds. The melt layer thickness gradually decreased with a decrease in environmental temperature and an increase in wind speed, with a maximum reduction of 25%. The difference between the tensile strength of the optimized sample and that of the indoor standard welding process was within 5% and met the standard welding quality requirements.

Aiming at the green path planning problem of picking and delivering automated guided vehicles (AGVs) with time windows constraints in the context of flexible manufacturing workshops, minimizing the energy consumption and time deviation energy consumption of AGV collection and distribution process as the combined optimization goal, the AGV green vehicle path planning model is constructed. According to the characteristics of the research problem, a hybrid genetic algorithm with improved variable neighborhood search (GA-VNS) is proposed to solve it, a series of five neighborhood structures are designed to improve the algorithm's optimization ability. The feasibility of the proposed algorithm in this paper is verified by solving the test set of Solomon benchmark and comparing the solution with the internationally best-known optimal solutions. Further, take the AGV logistics transportation task in a certain production period of a flexible manufacturing workshop as an experimental case, the algorithm designed in this paper, GA, and VNS algorithm are adopted respectively. Through a detailed analysis of experimental results, the optimization and applicability of the model and algorithm proposed in this paper are verified. It provides a feasible solution for the workshop to achieve the development goal of energy saving and emission reduction.

Aiming at the shortcomings of basic grey wolf algorithm(GWO) in robot path planning, such as falling into local extremum and low exploration efficiency, a multi-strategy improved grey wolf optimization algorithm was proposed. Firstly, a random walk strategy is proposed to improve the global search capability of the algorithm. At the same time, in the search stage, a reverse learning mechanism based on convex lens principle is introduced to reverse learn the inferior individuals in the population, so as to improve the hunt range of individuals of wolves and avoid the algorithm falling into local optimal. Finally, to improve the smoothness of the path, B-spline is used to smooth the path. The simulation results show that compared with the traditional gray wolf algorithm, the improved gray wolf algorithm has better performance in the global optimal path planning and is more conducive to the robot to complete the task in the common environment and trap environment.

Brief analysis of the domestic and foreign development present situation and the development characteristics of machine tool industry, machine tool industry are summarized in detail the development situation and problems faced by the surrounding the important strategy of China's economic and social development and national security needs, put forward the focus of the development of machine tool industry, According to the present situation of domestic machine tool industry, systematic development measures and suggestions are put forward.

The commissioning quality of CNC machine tools in the commissioning stage directly affects their machining accuracy. However, CNC machine tools in the service stage are rich and varied in processing scenarios, and as the representative equipment of complex electromechanical systems, they contain massive and complex coupling relationships. Therefore, the effectiveness of general commissioning methods in this case is very limited. Therefore, this paper proposes a way to simulate the complex machining scene of machine tool running in virtual environment and carry out virtual commissioning to get the best running parameters. Firstly, by analyzing the complex coupling relationship of electromechanical system, a digital twin model of CNC machine tools was constructed using multi-domain unified modeling language combined with virtual-real mapping strategy. Secondly, the complex machining scene was simulated based on the twin model, and the virtual commissioning method of NC machine tool was proposed. Finally, a numerical control machine tool spindle system is taken as an example to verify the effectiveness of the proposed method and the feasibility of field application. The experimental results show that the response time is reduced by 12% and the steady-state error is reduced by 54%.

Constructing robot simulation system and calibrating robot end tools can improve robot machining accuracy and realize fast trajectory programming. In this paper, the mirror relationship between the actual robot system and the robot simulation system is used to calibrate the end tool of the robot. Firstly, a calibration probe was installed at the end of the robot to measure the position and pose of the cuboid calibration block of known size in the robot space, and the image model of the cuboid was established in the robot simulation system. Secondly, the position and pose of the tool model relative to the end of the robot model are adjusted under the condition that the position and pose of the tool model relative to the edge and face of the cuboid model are consistent with that of the tool model relative to the edge and face of the cuboid model, and the mirror relationship of the tool model in the two systems is established. Finally, a calibrated tool is used to measure the blank, and the image of the blank is built by the measurement matrix in the simulation system, and the tool path conversion is realized in the simulation system. The experimental and practical machining results show that the calibration method has the advantages of simple calibration process and high calibration precision, and the dislocation error of two-way through groove machining is less than 0.73 mm.

In the high-speed railway sector, a machine tool is used as a working machine for part processing.The relationship between high-speed rail and the machine tool industry is summarized in this paper, which is followed by a hierarchical analysis of the types of machinable parts of high-speed rail, and finally a summary of the selection principles for general and special machine tools when machining high-speed rail mechanical parts. Then, it introduces the current mainstream special machine tools on the selection of processing technology for machining parts of high-speed rail machinery such as rails, bogies and car bodies. Finally, the development trend of machine tools in the field of high-speed rail is forecasted.

At present, the roughness of cutting surface needs to be combined with manual experience and multiple testing methods, and the machining quality is difficult to be guaranteed. On the basis of giving full play to the role of historical parameters in machining stage, a wear monitoring model was established. At the same time, in order to meet the requirements of the algorithm accuracy and response rate, we introduced the adaptive generalized regression neural network (AGRNN) for roughness prediction. The results show that the correlation coefficient between the calculated roughness prediction data and the actual value reaches R²=0.988, the prediction model reaches the ideal control state, the prediction accuracy meets the control standard, and the response time can be further shortened after the equipment adjustment. Spindle speed 1000～2000 r/min, feed 0.2～0.3 mm/r, axial cutting depth 0.2～0.4 mm, radial cutting depth 1～5 mm range, AGRNN corresponding wear and roughness MAPE of 3.685 and 2.236 in turn, It is lower than the four algorithms of convolutional neural network (CNN), Gaussian process regression (GPR), support vector machine (SVM) and multiple linear regression (MLR), achieving the ideal prediction effect and significantly shortening the control decision time.

Predictive maintenance is an important technology to predict the future working conditions of equipment through real-time monitoring of its operating status, and to realize fault diagnosis, life prediction, equipment maintenance and management. It is one of the typical applications of artificial intelligence in intelligent manufacturing. However, the confusion of terms and definitions related to predictive maintenance, the lack of communication and integration interfaces between systems, and the difference between monitoring diagnosis scheme and prediction algorithm have seriously hindered the application of predictive maintenance. Therefore, this paper analyzes and discusses the requirements of predictive maintenance standards from three aspects: predictive maintenance technology application, standard system, and standard content, constructs a predictive maintenance standard system covering basic common standards, key technical standards, and industrial application standards, and analyzes the current standards and the future direction of standardization. Based on the research in this paper, it is hoped that standards researchers can further develop standards related to data, evaluation, remaining life prediction, and maintenance management, and effectively promote the development of predictive maintenance technology and the digital transformation of equipment.

According to the characteristic of permutation flow-shop scheduling problem, a variable neighborhood bee colony algorithm based on crossover and selection strategy is designed. Firstly, the NEH heuristic algorithm is added in the initial population stage to improve the quality of initial solution. At the start of the algorithm iteration, for the purpose of improving the diversity of solution, differential evolution operator is added to crossover and selection. In the local search stage, two variable neighborhood operations of swap and inverse are added to 50% optimal individuals to enhance the search ability of the algorithm. Selecting appropriate parameters through orthogonal experiments, and conducting simulation experiments on Car, Rec and Taillard standard test sets, the results show that the proposed algorithm is superior to other swarm intelligence algorithms compared with it. Finally, the job scheduling problem on the tire production line of a company is solved with the optimization objective of minimizing the makespan. The results are better than the compared algorithm, which further verify the feasibility of the proposed algorithm in solving PFSP.

In the development of CAPP system integration, instrumentalization and intelligence, decision reasoning and intelligent technology of craft scheme are the key points. Traditional craft decision making method has the problem that it can not make intelligent choice in multi-craft scheme. To solve this problem, a craft decision reasoning method based on knowledge graph representation learning was proposed. In this method, the translation distance model is used to represent the craft knowledge vectorially, and the distance between the ideal result and the decision result is obtained by vector operation. After model training, the distance is constantly reduced, which leads to the stability of the loss function. Finally, the optimal result is obtained, and the rationality of the decision scheme is strengthened by fuzzy function calculation and analysis. The problem of intelligent decision in multi-craft scheme is solved. In this paper, P0 class 6203 bearing is used as an example to verify the proposed method. The results show that the proposed method can effectively realize the decision-making of the parts craft scheme and improve the ability of intelligent decision-making of the CAPP system.

Aiming at the problem of poor follow-up effect of the rehabilitation robot arm in the rehabilitation teaching process, an admittance controller combining the torque differential term in the joint space is designed, and an improved variable admittance control strategy is designed in combination with the hyperbolic tangent function. The teaching simulation was carried out on the Adams and MATLAB/Simulink joint simulation platform, and a physical teaching experiment platform was built for verification. The results show that the variable admittance controller can better realize the teaching function, while reducing the trajectory following error and the size of the human-computer interaction torque, improving the follow-up effect, and the teaching repeatability.

In order to quickly determine the full-process forming process plan for large automobile panels, shorten the number of full-process forming simulation iterations and calculation cycles, and improve the full-process forming simulation accuracy, the side outer panel of a certain car model was taken as the research target. According to the product structure characteristics, the side outer panel was divided into several regions, combined with the process characteristics of each region the key section parameters of the die surface was drew, the die surface was designed efficiently by using the key section parameters, and the feasibility of the full process plan was verified by using the finite element analysis software. The results shown that the side outer panel were fully formed as a whole, without cracks, wrinkles, and obvious surface defects and other forming problems. On this basis, the die tryout was verified, and the samples with good forming effect and basically consistent with the numerical simulation results were obtained. Research has shown that after applying the die surface engineering technology based on the key section parameters-driven die surface design, the side outer panel does not need to carry out die surface design, repeated adjustment and optimization in the finite element analysis software, which significantly improves the simulation accuracy and efficiency of the full process of the side outer panel.

Aiming at the integrated scheduling problem of production and logistics resources in flexible workshop, a niche adaptive genetic algorithm based integrated scheduling method is proposed. First of all, logistics process, path conflict elimination method and allocation strategy of the AGV in flexible workshop are defined. Secondly, the integrated scheduling problem of workshop production and logistics resources is described, and an optimization model aiming at the shortest completion time is established. Then, the niche technology and adaptive strategy are introduced into the genetic algorithm to make the genetic strategy change with niche characteristics adaptively. And a new niche adaptive genetic algorithm (NAGA) is proposed. Finally, a niche adaptive genetic algorithm based two-resource integrated scheduling process is developed. Through experimental verification, the completion time of niche adaptive genetic algorithm scheduling is shorter than that of genetic algorithm (GA) and improved distribution estimation (IEDA) algorithm in literature [1], which indicates that the integrated scheduling performance of NAGA algorithm is better than that of GA algorithm and IEDA algorithm. According to the impact analysis of the number of AGVs, the completion time of the workshop decreases with the increase of the number of AGVs, and the completion time does not decline when the number of AGVs is saturated.

This paper introduces the application and development trend of green technology in machine tool equipment, and analyses the current situation of green machine Tool standardization at home and abroad. Based on the research of green machine tool standard system and the method of standardization, the frame of green machine tool standard system is constructed. It is expected that the green technology of machine tool and standardization work can realize coordinate development, to promote the green transformation and improving quality and efficiency of machine tool industry.

The thermal steady state simulation analysis models for the solid ball screw system and for the hollow ball screw system were established respectively first based on the determination and computation of the heat source and thermal boundary conditions of the ball screw system. Besides, the finite element method was applied to obtain the thermal steady temperature field distribution. Then, the thermal deformation status of solid ball screw and hollow ball screw was obtained through the thermal structure coupling analysis on the ball screw system with multi-field coupling, and the influence of thermal deformation on the ball screw system was analyzed.After that, the modal analysis and static analysis on the solid/hollow ball screw system were performed respectively to study the changes of the static and dynamic characteristics of the ball screw after being hollowed out. By doing so, it would provide theoretical evidence for the optimization and improvement of the vertical machining center.

The connotation and development mode of intelligent manufacturing are studied. By adopting the descriptive case study and taking the intelligent manufacturing development of Sany Group as an example, the development mode at various stages and the construction of the Internet of Things platform have been studied and analyzed comparatively. The intelligent manufacturing transformation process of Sany Group is summarized as three stages，including product intelligence, manufacturing intelligence and service intelligence. Based on this, some enlightenments are put forward for Chinese manufacturing enterprises to carry out intelligent manufacturing transformation and develop industrial Internet of things.

Automatic process model creation is the core function of the 3D part machining process planning system. In order to improve the speed and quality of three-dimensional process model modeling, this paper proposes an automatic modeling method based on the featured cutting body, which means the operation model of the current operation is “subtracted” from the previous operation model. Firstly the correlation is established between the process model and the process by using the machining features and machining feature work steps, and the feature cutting body is obtained by matching the machining features in the cutting body library. It instantiates the feature cutting body according to the dimensional and positioning parameters of the machining features, and determines its defining parameters and positioning parameters to finally form the cutting model; then does the Boolean subtraction operation between the previous process model and the feature cutting body model to obtain the process model of the current process. This approach takes machining features and feature cutting body as the core, blank model and process information as the basis, and margin size as the driver, and generates each process model according to the actual processing order in a forward direction. This forward process model generation method is consistent with the real part processing process, and after application verification in enterprises, it fully meets the enterprise application requirements, greatly reduces the workload of process personnel in the process model, so that they have more resource and time for improvement and innovation of the process. It also could be used in improving the process level and capability of Chinese manufacturing enterprises.

In complex flexible manufacturing systems, it is important to conduct reasonable autonomous mobile robot (AMR) scheduling. In this study, the path planning and task assignment of AMR scheduling are studied to minimize the time for AMR to complete tasks. A mathematical model is established with the minimum AMR task completion time as objective function, and the map is modeled based on topology method. A greedy algorithm is used to assign tasks for orders with a constant length distribution for the interval time, and the algorithm calculation is reduced by classifying the AMR working state. Global path planning is based on the Dijkstra algorithm to search for the global shortest path of the AMR, and local obstacle avoidance path planning is performed through the AMR's laser radar. Finally, the effectiveness is verified through scheduling simulation experiments on the openTCS platform.

In high-end manufacturing areas such as transportation, mining, and petrochemicals, shaft components often failed due to wear, corrosion, and fatigue under the high temperatures and pressures. Nowadays, laser cladding is widely used for shaft components surface repair and strengthen because of its low dilution rate, small heat-affected zone, and good metallurgical bonding between the coating and the substrate. This paper provides an overview of the commonly used process methods for laser cladding, including pre-set powder feeding, simultaneous powder feeding and wire feeding cladding, and provides researchers with an initial understanding of this direction through an introduction to the above processes. At the same time, the performance of the cladding layer of shaft parts processed under different process parameters, such as the influence of comprehensive parameters such as laser power, scanning speed, lap rate and powder feed rate, is reviewed, as well as the application of numerical simulation techniques to the simulation of stress fields when laser cladding processing is carried out on the surface of shaft parts. The research on ultra-high speed laser cladding of shaft components is discussed. Finally, the problems in the current research on laser cladding of shaft components are summarized and the development directions are outlined.

Surface roughness is the main index to measure the surface quality of micro machined parts. In order to improve the accuracy of surface roughness prediction in micro milling, a deep neural network prediction model based on one-dimensional convolution-long short-term memory (1DCNN-LSTM) is proposed. Using the efficient data processing mechanism of one-dimensional convolution network and the accurate prediction ability of long-term and short-term memory network, the problems of batch sequence data processing, sample key feature learning and small sample data surface roughness prediction are effectively solved. Taking spindle speed, feed speed, milling depth and micro milling cutter spiral angle as control variables, the prediction model of micro milling surface roughness is trained and verified by experimental data. The results show that compared with the traditional machine learning model, the average prediction error of 1DCNN-LSTM neural network is only 5.9%, which verifies the high-precision prediction performance of the model based on small sample data, and provides a new method for the prediction of micro-milling surface roughness.

A data acquisition architecture that integrates unit devices into a unified OPC UA architecture for communication is designed in order to address the problem of data acquisition difficulties and response time extension, etc. during a large number of acquired communication protocols data for smart made units of dropper pre-assembly of high-speed railway is combined, then to be centralized and transferred to the cloud for analysis. An end-to-end motor fault real-time diagnosis method applied to the edge field has been proposed by combining the characteristics of motor vibration data in the cloud after the collected data is preprocessed at the edge nearby and then coordinated to the cloud. The application of the proposed method in the cantilever pre-assembly production line has been verified, and the results show that the data acquisition and fault analysis method is universal and efficient.

Welding 5 mm thick 6061-T651 aluminum alloy with laser-TIG composite heat source filler wire, investigate the influence of arc current on weld formation of composite filler wire welding, analyzed the weld microstructure and microhardness characteristics under the optimized process parameters,and compared with single TIG filament welding. The results show that the use of laser-TIG arc composite heat source filler wire welding of 6061-T651 aluminum alloy can effectively improve the weld formation; when the TIG arc current is 140 A, the welding process is stable and the welding seam forming effect is good; the microstucture in the center area of the composite filler wire weld is equiaxed, and the fusion zone is compo sed of a large number of dendrites; the microhardness of composite filler wire welding is higher t han that of single TIG filler wire welding, there is a softening phenomenon in the weld area,within the range of selected test points, the average hardness of the weld center of the composite filler wire welding is 66.91 HV, which is about 62.0% of the hardness of the base material, which is about 13.1% higher than the microhardness of the TIG filler wire welding alone.

Aiming at the problem of inaccurate workpiece position in the assembly process of infrared sensor, an automatic assembly system of infrared sensor based on visual recognition and positioning technology is designed. The mechanical structure of the system is composed of industrial robot, feeding unit, conveying unit, visual detection unit, quick-change tool unit, the seventh axis of industrial robot, etc. The position and angle of the sensor end cover are recognized by the vision system, and the result is converted to the robot world to make the workpiece can be grasped accurately. A PLC control system based on Ethernet communication is built to complete the discharging, conveying, grabbing and automatic assembly control of infrared sensor parts. The experiment shows that the designed system has high degree of automation, reliable operation and high promotion application value.

Thermal error seriously affects the machining accuracy of the machine tool. The thermal characteristic analysis of the key parts of the machine tool is an important link in the development of precision machine tool. Therefore, this paper studies the relationship between temperature rise and positioning error by measuring the thermal characteristics including the temperature and positioning error of special position of CNC machine tool, and proposes a thermal error modeling method based on Bayesian neural network. Using K-means clustering and correlation coefficient method to select temperature sensitive points can effectively suppress multicollinearity between temperature measurement points. The results show that the accuracy of machine tool can be improved by 88.015 9% by using bayesian neural network, which is 15.763 8% higher than BP neural network. Compared with BP neural network model, bayesian neural network has better prediction performance. Bayesian neural network model provides a new idea to reduce the influence of thermal error of machine tool.

In order to improve the accuracy of stereo structured light 3D reconstruction system, a point cloud error compensation method is proposed. The method is divided into three parts: error calibration, error modeling and error compensation. Firstly, a new error calibration method is proposed, which divides the measurement space of stereo vision structured light system into discrete feature points, and calibrates the error of feature points in the whole space. Secondly, the error modeling method based on neural network is proposed, and the error model of this space is established. Finally, a point cloud error compensation method suitable for stereo vision structured light system is proposed. After experiments, it is proved that the proposed error compensation method reduces the diameter error by 51.96% and the sphere center distance error by 14.16%, respectively. The effectiveness and feasibility of this algorithm are also verified.

Based on the project of efficient production line, this paper aims to propose an improving A* path planning algorithm which is applied to the motion control of AGV. After detecting obstacles, it can be used in real-time path planning. So the safety of AGV robots application is improved without affecting the efficiency of transport. In this paper, the working environment of the AGV robot is analyzed, and the environmental model is established. Then the traditional A* algorithm was explained and analyzed, and the improvement measures were put forward for its shortcomings. Finally, the validity of the algorithm is verified by simulation.

In order to solve the problem of low efficiency and low intelligence of feature recognition in traditional computer-aided process planning (CAPP) system, this paper proposed a novel machining feature recognition method for mechanical parts based on Mesh-Faster Region-based CNN (RCNN) by combining original MeshCNN with Faster RCNN. The method obtained the optimal neural network model by taking the customized processing dataset as the input of the neural network. Then MBD technology was used to label the machining model, and the characteristics to be processed were obtained by PMI information annotation, which were transformed into triangular mesh data. On this basis, combined with the algorithm of triangular mesh data processing, the processed machining feature data is imported into the optimal neural network model to complete the feature recognition process. Finally, the feasibility and effectiveness of the proposed method are verified by taking the key parts of the diesel marine engine as an example.