基于SVR-GA算法的广义加工空间机床切削稳定性预测与优化研究

针对机床零件加工位置和进给方向不确定造成刀尖频响函数变化,导致切削稳定性叶瓣图与无颤振工艺参数预测具有不确定性问题,提出一种耦合支持向量回归机(SVR)与遗传算法(GA)的切削稳定性预测与优化方法。该方法采用锤击法模态实验和空间坐标变换,获取样本空间不同加工位置与进给方向的刀尖频响函数;进而结合传统切削稳定性预测方法构建以各向运动部件位移、进给角度、主轴转速、切削宽度、每齿进给量为输入的极限切削深度SVR预测模型;采用该SVR模型作为切削稳定性约束建立材料切除率优化模型,通过遗传算法求解各运动轴位移、进给角度与切削参数的最优配置。以某型加工中心展开实例研究,实验结果表明获取的优化配置能实现稳定切削,验证了该方法的有效性。

Research on the prediction and optimization of machine tool cutting stability in generalized manufacturing space based on support vector regression machine and genetic algorithm

Aiming at the problem that the uncertainty of part processing position and feed direction of machine tool causes the change of the tool tip frequency response function (FRF), which leads to the uncertainty of cutting stability lobe diagram and chatter free processing parameter prediction, a cutting stability prediction and optimization method is proposed combining the support vector regression (SVR) machine and genetic algorithm (GA). This method adopts the hammer impact modal test and spatial coordinate transformation to obtain the tool tip FRFs of different machining positions and feed directions in sample space; then combining the traditional cutting stability prediction method, a SVR prediction model of the limiting cutting depth is established, which takes the displacements of machine tool moving parts, the feed angle, spindle rotation speed, cutting width and the feed rate per tooth as the inputs; the SVR model is taken as the cutting stability constraint to establish the optimization model for the material removal rate (MRR); with the genetic algorithm (GA), the optimal configuration of the displacements of the moving axes, feed angle and cutting parameters is solved. A case study was performed on a certain machining center, and the experiment result shows that the obtained optimal configuration can achieve stable cutting, which verifies the effectiveness and feasibility of the proposed method.