TY - JOUR
T1 - Statistical optimization and assessment of a thermal error model for CNC machine tools
AU - Lee, J. H.
AU - Yang, S. H.
PY - 2002/1
Y1 - 2002/1
N2 - The objective of a thermal error compensation system for CNC machine tools is improved machining accuracy through real time error compensation. The compensation capability depends on the accuracy of the thermal error model. A thermal error model can be obtained using an appropriate combination of temperature variables. In this study, the thermal error modeling is based on a correlation grouping and a successive linear regression analysis. During the successive regression analysis, the residual mean square is minimized using a judgement function, which, although simple, is effective in the selection of variables in the error model. When evaluating the proposed thermal error model, the multi-collinearity problem and computational time are both improved through the correlation grouping, and the linear model is more robust against measurement noises than the engineering judgement model, which includes variables with higher order terms. The modeling method used in this study can be effectively and practically applied to real-time error compensation because it includes the advantages of simple application, reduced computational time, sufficient model accuracy, and model robustnesss.
AB - The objective of a thermal error compensation system for CNC machine tools is improved machining accuracy through real time error compensation. The compensation capability depends on the accuracy of the thermal error model. A thermal error model can be obtained using an appropriate combination of temperature variables. In this study, the thermal error modeling is based on a correlation grouping and a successive linear regression analysis. During the successive regression analysis, the residual mean square is minimized using a judgement function, which, although simple, is effective in the selection of variables in the error model. When evaluating the proposed thermal error model, the multi-collinearity problem and computational time are both improved through the correlation grouping, and the linear model is more robust against measurement noises than the engineering judgement model, which includes variables with higher order terms. The modeling method used in this study can be effectively and practically applied to real-time error compensation because it includes the advantages of simple application, reduced computational time, sufficient model accuracy, and model robustnesss.
KW - Correlation grouping
KW - Judgement function
KW - Multi-collinearity
KW - Regression analysis
KW - Robustness
KW - Thermal error model
UR - http://www.scopus.com/inward/record.url?scp=0036027385&partnerID=8YFLogxK
U2 - 10.1016/S0890-6955(01)00110-9
DO - 10.1016/S0890-6955(01)00110-9
M3 - Article
AN - SCOPUS:0036027385
SN - 0890-6955
VL - 42
SP - 147
EP - 155
JO - International Journal of Machine Tools and Manufacture
JF - International Journal of Machine Tools and Manufacture
IS - 1
ER -