基于人工智能算法的最优加工表面粗糙度预测研究

摘要

以切削速度、 进给量、 切削深度、 刀尖圆弧半径为设计变量,采用正交试验法进行了立方氮化硼(CBN)刀具干式车削冷作模具钢Cr12MoV的试验研究.利用神经网络的非线性拟合能力和遗传算法的全局寻优能力,建立了加工表面粗糙度预测模型并获得了使表面粗糙度达到最优的切削用量与刀尖圆弧半径组合.利用遗传算法获得的最优表面粗糙度值比田口方法和切削试验所获得的最佳表面粗糙度值分别降低了7.1%和17.2%.文中所采用的方法也为切削加工中刀具磨损、 切削力和残余应力等问题的建模与参数优化提供理论参考.%Dry turning of Cr12MoV cold work die steel with cubic boron nitride (CBN) cutting tools is experimentally investigated using Taguchi orthogonal experiment method , in which the cutting speed , feed rate, depth of cut, and tool nose radius were considered as design variables .By making use of the nonlinear fitting ability of neural networks , coupled with the global searching ability of genetic algorithms, a model for predicting the machining surface roughness was established and an optimal combination of cutting parameters and tool nose radius giving the optimal surface roughness was found .The value of the optimal surface roughness obtained by genetic al-gorithms was reduced by 7.1%and 17.2%, respectively , as compared to the values of the optimal surface roughness obtained from the Taguchi method and turning experiments .The method used here provides theoretical reference for the modeling and parameter optimiza -tion of tool wear , cutting force and residual stress , and other problems in cutting process .