Research On Tool Intelligent Management System And Tool Condition Monitoring Technology Of Torque Converter Production

Enable production and processing with artificial intelligence is the future development trend and inevitable requirement of the manufacturing industry.It is an important way to accelerate industrial informatization,promoted the transformation and upgrading of the manufacturing industry.At the same time,it highly conforms to the new development requirements of the manufacturing power strategy "Made in China2025".Effective monitoring of CNC machine tools and other basic manufacturing equipment in the production process is an important means to improve manufacturing technology.At present,the production mode of most domestic enterprises is mainly based on discrete production line processing with relatively independent functional modules.The information fusion degree among the production modules is sparser,and the production efficiency of enterprises is relatively low.Due to the backward information transmission and integration mode,it is difficult to maximize the workshop production potential,resulting in the waste of production resources of enterprises.The application of artificial intelligence technology can effectively accelerate the improvement and upgrading of the equipment manufacturing industry from the leading mode of discrete manufacturing to the intelligent and information production mode.However,bottleneck technical problems,such as equipment sensing interconnection of things in the production line of the workshop,production data collection,storage and modeling,and big data analysis and mining in the production process,need to be solved urgently.According to the analysis of technical bottleneck problems,based on the discrete type motor torque converter manufacturing workshop production line as the research object,study of torque converter full cycle of the production process,according to the ideas of the distributed integration,and through the Internet of information fusion and artificial intelligence technology to build intelligent management system,the torque converter production process to the key design research and development of tool management subsystem,The tool condition management and monitoring technology in the subsystem were studied emphatically,and the tool wear quantity was proposed as the reference basis for tool condition management.Meanwhile,the tool wear turning force model and the tool condition intelligent monitoring model were established,so as to realize the research of cutting force tool condition monitoring technology.The main research contents of this paper are as follows:(1)Taking the discrete production line of automobile torque converter as the research object,this paper analyzes the current situation of discrete workshop management of enterprises,obtains the reasons for the low production efficiency of enterprises at the present stage,and puts forward the new requirements to adapt to the production process management of future factories.At the same time,the intelligent management system of automobile torque converter production line is designed and developed.(2)The torque converter is analyzed the new demand for cutting tool management intelligent management system,according to the cutting tool geometry parameter and cutting tool inventory information such as the tool of static and dynamic information model is established,and on this basis,in combination with coding,list of tools and tool assembly and component technology designed and developed for tool management system,the torque converter production workshop tool management,at the same time,the tool wear quantity is taken as the evaluation standard of tool condition management.(3)The tool wear mechanism was studied,and the change law of tool wear form under different factors was obtained.Based on the metal cutting principle and the Elasto-Plastic contact mechanism of tool/workpiece,the turning force model considering the wear factor of tool back surface was established,and the mapping relationship between tool wear and cutting force was obtained.(4)The numerical simulation model of tool wear turning force was established,and the influence law of different cutting parameters on turning force was obtained.The accuracy of the numerical simulation model of tool wear turning force is proved by the error between the experimental turning force and the numerical simulation results calculated by turning experiments with different wear grades of tools.Through the analysis of workpiece surface forming quality,the influence law of tool surface wear on turning process and surface forming quality is obtained.(5)Experiment explores the different machine learning algorithms performance of cutting tool state monitoring model,this paper presents a new cnn-xgboost tool condition monitoring method,through the confusion matrix,root mean square error,mean absolute error as the evaluation index,proved that the new algorithm in the tool wear condition monitoring and cutting tool residual life prediction,The test performance of the new algorithm in tool wear condition monitoring and tool residual life prediction was evaluated.