Research On Multi-sensor Signal Feature Fusion And Parameter Optimization Of Titanium Alloy Cutting

Titanium alloys are widely used in aerospace,biomedical and other high-precision industries because of their high specific strength,excellent chemical corrosion resistance and excellent biocompatibility.Compared with other metals,titanium alloys are a difficult material to cut.In the process of machining,the increase of cutting temperature,the aggravation of cutting vibration and the change of cutting noise affect the machining quality and cutting environment of the workpiece.Therefore,on the basis of turning and milling experiments,the cutting temperature,cutting vibration,cutting noise and multiobjective parameter optimization of titanium alloy were studied.The main research contents are as follows:(1)A synchronous acquisition system for cutting temperature,cutting vibration and cutting noise was established.The temperature of the contact area between the tool tip and the work-piece was collected by an infrared thermal imager,and the data of cutting vibration and noise were collected by WS-AV acoustics and vibration measurement system.Based on the wavelet packet analysis,the cutting vibration signals were denoised and the corresponding characteristic values were extracted.According to the sound pressure value of cutting noise,the sound pressure level value is obtained.According to the maximum temperature time domain value of the contact area between the tool tip and the work-piece,the maximum average temperature value of each cutting test is obtained.(2)The variation law of cutting signal characteristics of titanium alloy under different cutting parameters was analyzed,and the regression models of cutting temperature,cutting vibration and cutting noise on cutting parameters were established by using response surface method.Coupling analysis was carried out on the cutting signal characteristics of titanium alloy,and the coupling coordination degree between double and triple signals in turning and milling were calculated respectively.The results show that the coupling effect of the signals is stronger in turning and weaker in milling of titanium alloy.(3)The standard particle swarm optimization algorithm(PSO)is improved in three aspects,and three classical test functions are used to compare and analyze the performance and optimization efficiency of PSO before and after the improvement.A multi-objective optimization model with the minimum cutting temperature,the minimum cutting vibration,the minimum cutting noise and the maximum metal removal rate was established.The improved particle swarm optimization algorithm was used to optimize the multi-objective model,and the optimal solution of the optimization variables was obtained.