Experimental Research On Milling Of Ti-48Al-2Cr-2Nb Alloy Microgrooves

As a new ?-TiAl alloy,Ti-48Al-2Cr-2Nb alloy has many excellent properties,such as low density,high melting point,oxidation resistance and high temperature mechanical properties,and has a broad application prospect.However,there are few researches on the cutting performance of this material,and the research on the micro-milling characteristics is even less reported.Therefore,the research on the micro-milling characteristics of this material is of great significance to the promotion and application of this material.In this paper,the effects of cutting parameters on micro-milling force,surface roughness at the bottom of micro-groove and burr at the top of micro-groove were studied by micro-groove milling experiments.A prediction model for surface roughness of micro-groove milling was established based on LSTM.The main research contents are as follows:Ti-48Al-2Cr-2Nb alloy micro-groove milling experiment was designed to study the milling force in the process of micro-milling,the influence of different cutting parameters on the milling force was analyzed,and the Ti-48Al-2Cr-2Nb alloy micro-milling force empirical model was established by multiple linear regression equation to predict the micromilling force.Based on the Ti-48Al-2Cr-2Nb alloy microgroove milling experiment,the side top burr and the bottom surface roughness were studied.The influences of various cutting parameters and tool helical Angle on the burr and surface roughness were analyzed.The parameters selection principle for reducing or inhibiting burr and improving surface roughness was obtained.Based on one-dimensional convolution-long short-term memory neural network,a prediction model of the surface roughness of the micro-groove bottom was established.The model was trained with experimental data to achieve accurate prediction of the roughness of the micro-groove bottom.A multi-objective optimization model of micro-groove milling was established with smaller milling force,smaller surface roughness and larger material removal rate as the optimization objectives.The third generation non-dominated sorting genetic algorithm(NSGA-?)was used to solve the optimization model,and the reasonable optimization results were obtained,which provided a reference for the actual production.