北京航空航天大学学报 ›› 2000, Vol. 26 ›› Issue (4): 457-460.

• 论文 • 上一篇    下一篇

刀具磨损的偏最小二乘回归分析与建模

刘强   

  1. 北京航空航天大学 机械工程及自动化学院
  • 收稿日期:1999-05-31 发布日期:2010-11-19
  • 作者简介:刘 强(1963-),男,湖南省耒阳人,副教授,100083,北京.
  • 基金资助:

    国家自然科学基金资助项目(59975008)

Partial Least-Squares Regressive Analysis and Modeling for Tool Wear

LIU Qiang   

  1. Beijing University of Aeronautics and Astronautics,School of Mechanical Engineering and Automation
  • Received:1999-05-31 Published:2010-11-19

摘要: 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Fx、Fy和Fz、分力比值Fy/Fx和Fz/Fx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.

Abstract: 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 Fx, Fy and Fz, as well as force ratios Fy/Fx and Fz/Fx, 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.

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