Research On The Mold Cutting Database And Data Mining Technology Of Automotive Covering Parts Hardened Steel Mold

With the introduction of “Germany's Industry 4.0 and “Made in China 2025”,the informationization of manufacturing industry and intelligent manufacturing have become the mainstream.As an inevitable trend of intelligent manufacturing,intelligent database has become an important way to improve manufacturing efficiency,processing quality,economic benefit and reduce production cost.The automotive industry has become a pillar industry in the manufacturing industry,and the main body of automobile body is automobile panels.The renewal cycle of the model is shortened so that a large number of molds are replaced.In this paper,considering the complexity of automobile panel dies,too many process steps,the low degree of information sharing,the strong dependence of parameter selection,the unpredictable results and the low efficiency,the intelligent processing database of hardened steel mold for automotive panel is established based on WEB.In order to provide theoretical guidance and data support for improving processing efficiency and reducing production cost,the theory and application of data mining technology are studied.Firstly,the overall process of the data preprocessing of the database is studied.In view of the many kinds of cutting data and the intricate of interaction factors,combining with the examples,the preprocessing of cutting data is studied,and the methods and processes of data source,data exploration and data preprocessing are obtained.Secondly,the method of cutting data predictive is studied.The finite element software is used to establish the milling simulation model.The reliability of the finite element simulation is verified by Cr12 Mo V plane milling experiment.Combinating regression analysis and prediction method of GA-BP neural network,the prediction model of milling force is established,and the results of prediction and the experiment are compared and analyzed to verify the credibility of the prediction model.Thirdly,the optimization of cutting data is studied.The optimization model of cutting process is established by using NSGA-II algorithm.The curved surface milling experiment of Cr12 Mo V mold steel is carried out to analyse the effectiveness of the optimization model.The influence of milling parameters on milling force and surface roughness are analyzed.Taking the milling force,surface roughness and material removal rate as the objective function,the multi-objective optimization is carried out through the NSGA-? algorithm,and verified through experiments.Finally,the overall functional requirements of the automotive covering parts database are analyzed through the theory and methods of data mining techniques.WEB intelligent vehicle covering parts database system is developed,and the database system's various functional modules and operating procedures are expounded by using JAVA and MYSQL as the basic development tools and JAVA lightweight framework combination Spring MVC + Spring + Mybatis as the basic framework.