刀具磨损的偏最小二乘回归分析与建模

简述了偏最小二乘回归方法;对在不同切削条件下车削加工过程刀具后刀面磨损的多组实验数据,采用偏最小二乘回归方法,根据变量重要性指标分析和因子载荷分析,从8个变量及其组合中筛选出了6个用于建模的自变量,并以后刀具磨损量作为因变量,建立了对所选自变量(切削速度V、切削分力的均值F_x、F_y和F_z、分力比值F_y/F_x和F_z/F_x等)的偏最小二乘回归模型;采用建模数据覆盖的切削条件下的实验数据和建模数据未覆盖的切削条件下的实验数据,分别对模型进行了验证.结果表明,采用偏最小二乘回归方法选择的自变量是合理的,所建立的刀具磨损的回归模型可以较满意地计算出不同切削条件下刀具后刀面的磨损量.

Partial Least-Squares Regressive Analysis and Modeling for Tool Wear

The algorithm of partial least-squares regression(PLSR) is briefed firstly. The PLSR analysis is applied to the sample data sets of cutting tool wear under different machining conditions. Six independent variables for modeling including cutting speed V , cutting force components F x, F y and F z , as well as force ratios F y/F x and F z/F x , are screened from eight original variables based upon the variable important projection and the factor loading. The model with the six independent variables and the flank wear of cutting tool as the dependent variable is built up by using PLSR approach. Two sample data sets, one under the cutting conditions covered in the modeling data and the other under new different cutting conditions, are used to verify the model respectively. The results demonstrate that the variable screening is reasonable and the satisfied values of the flank wear of cutting tools can be obtained from the PLSR model.