Research On Parameters Optimization And Data System For Milling TC4 With Micro-Textured Ball-End Milling Cutter

Titanium alloy has many advantages,such as high specific strength,good heat and corrosion resistance,and is widely used in various fields.However,titanium alloy,as a typically difficult-to-machine material,has severe tool wear during the cutting process,which affects the processing quality,resulting in low processing efficiency and high production costs.The research shows that the cutting parameters and microtextured parameters have an important impact on the cutting performance of the tool when milling TC4 with micro-textured ball-end milling cutter.Selecting reasonable parameters can effectively reduce tool wear and improve machining quality.However,at this stage,the research of micro-textured tools faces a series of problems,such as the selection of parameters excessively depends on a large number of experimental experience and processing manuals,the selection parameters is difficult,and the research cycle of micro-textured tools is limited by the modeling efficiency of microtextured tools.In order to get rid of the above problems,this paper completes the micro-texture parametric modeling of UG internal ball-end milling cutter based on the micro-textured distribution theory;The prediction model is established through experiments to predict the milling performance,and the optimization algorithm is introduced to solve the problem of parameter optimization.Finally,the data system for milling titanium alloy with micro-textured ball-end milling cutter is built,and the following research is carried out:This paper first establishes the micro-textured distribution model on the rake surface.Based on the micro-textured distribution model,the UG is developed by using the program-driven method,and the function callback between the user interface and the parametric model is realized by using the UG customized menu and dialog box.The micro-textured parametric design of ball-end milling cutter is completed,which improves the efficiency of the micro-texture design and shortens the research cycle of the micro-texture cutter.Secondly,taking milling force,tool wear and surface roughness as the research objectives,an orthogonal test platform for milling titanium alloy with micro-textured ball-end milling cutter is built to study the influence of micro-texture parameters and cutting parameters on milling performance.Then,the milling performance prediction model is established by empirical regression analysis,stepwise regression analysis and BP neural network respectively,and the optimal prediction model is selected based on the root-mean-square error as the evaluation standard.It is concluded that the fitting degree of the stepwise regression model is higher than the other two models.Therefore,the stepwise regression model is used as the objective function and the genetic algorithm is used to establish the optimization model of micro-textured parameters and cutting parameters,and the multi-objective optimization based on multiple evaluation indicators is realized.The reliability of the optimization model is verified by experiments.Finally,based on the above research contents,on the basis of clarifying the logical relationship between the functional requirements and the database,a data system for milling titanium alloy with micro-textured ball-end milling cutter is established based on the Sqlite database under the python programming environment to realize the main functions of information query and management,micro-texture distribution design,milling performance prediction,parameter optimization,etc.Through this research,the rapid modeling of the micro-texture of ball-end milling cutter based on UG is realized,which can predict the milling performance under the specific working conditions given by the user in the processing process.Through parameter optimization,the practical problem of relying on experience and a large number of experiments to select parameters in traditional processing is improved,and the modeling efficiency and production efficiency are improved,providing a reference for actual production.