Data-driven Cnc Machining Quality Analysis And Process Parameter Optimization Of Flat-slit Antenna

As one of the key components of the military radar,the core parts of the plate crack antenna are vulnerable to deformation and dimensional instability during the cutting process due to structural shortcomings such as weak rigidity and low profile,which seriously affects the electronic performance of the radar.As an important aspect of improving the processing quality of flat crack antennas,process parameters directly affect performance indicators such as processing efficiency,processing quality,and tool life.Therefore,exploring the influence of process parameters on the quality of workpieces,taking the processing quality of multiple processing characteristics as the optimization goal,realizing the simultaneous optimization of multiple process parameters,guiding actual production,has important engineering application value.In the process of CNC machine tool processing,the selection of traditional process parameters often relies on manual experience,and there is a lack of a method for simultaneous quantitative optimization of multiple process parameters driven by optimization goals,and it is difficult to achieve effective control analysis in the process.Therefore,based on historical processing data,this thesis proposes a data-driven method for the quality analysis and process parameter optimization of the flat slot antenna numerical control processing.Obtain the mapping law between the multi-processing features and multi-processing parameters of the flat-slit antenna parts,and establish the quantitative function relationship between the processing parameters and the processing quality.Based on this,combined with intelligent optimization algorithms to achieve simultaneous optimization of multiple processing parameters,search for the combination of process parameters that best meets the requirements of processing quality,and provide a basis for the process parameters and quality prediction of CNC machine tools.The main research contents are as follows:(1)Analysis of factors influencing the cutting process of flat-slit antenna parts and optimization frame design of multi-process parameters.Due to the high-density processing characteristics of flat-slit antenna parts,it is difficult to select process parameters that meet the quality requirements of multiple processing characteristics in the actual processing process.To solve this problem,on the basis of combing the influencing factors that affect the machining quality in the CNC machining process,a multi-process parameter optimization framework for the CNC machining of flat-panel antenna parts driven by historical data is constructed.(2)The machining quality prediction model of XGBoost multi-feature thin-wall parts based on Bayesian optimization was constructed to form the multi-quality objective function of parameter optimization.The machining dimensions of different machining features were obtained by taking the cutting parameters as the input of the machining quality model.The mapping relationship between the machining parameters and the machining quality was established by using the machine learning algorithm trained by the data.(3)Design a quality-oriented multi-process parameter synchronous optimization method for NC machining.The cutting parameter optimization variables and constraint conditions are designed according to the machining mechanism knowledge,and the cutting parameter optimization model is reasonably constructed.The Metropolis Criterial-based genetic optimization algorithm is designed to solve the optimization model,and reasonable process parameters are recommended to guide the setting of machining parameters,so as to improve the machining quality control level of CNC machine tools.This thesis proposes a data-driven numerically controlled machining quality analysis and process parameter optimization method for a flat slot antenna.Combining machine learning models and intelligent optimization algorithms,the effective and in-depth integration of process parameter recommendation models,historical processing data,and business knowledge is realized.And the feasibility of optimizing processing parameters based on data-driven machine learning modeling method and intelligent optimization algorithm is verified through examples.