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�������պ����ѧѧ�� 2000, Vol. 26 Issue (4) :457-460    DOI:
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Partial Least-Squares Regressive Analysis and Modeling for Tool Wear
LIU Qiang*
Beijing University of Aeronautics and Astronautics,School of Mechanical Engineering and Automation

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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.
Keywords�� cutting tests   cutting tool   data processing   regression analysis   partial least-squares     
Received 1999-05-31;


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��ǿ.����ĥ���ƫ��С���˻ع�����뽨ģ[J]  �������պ����ѧѧ��, 2000,V26(4): 457-460
LIU Qiang.Partial Least-Squares Regressive Analysis and Modeling for Tool Wear[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2000,V26(4): 457-460
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