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Gram-Schmidt regression and application in cutting tool abrasion prediction

Huiwen Wang 1 Meiling Chen 1 Gilbert Saporta 2 
2 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage
CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : Multiple linear regression is one of the most widely applied statistical methods in scientific research fields. However, the ordinary least squares method will be invalid when the independent variables set exists server multicolinearity problem. A new multiple linear regression method, named Gram-Schmidt regression, was proposed by the use of Gram-Schmidt orthogonal transformation in the modeling process. Not only can it screen the variables in multiple linear regression, but also provide a valid modeling approach under the condition of server multicolinearity. The method was applied to the prediction of the flank wear of cutting tool in the turning operation. The results demonstrate that the variable screening is reasonable and the model is highly fitted.
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Submitted on : Friday, March 13, 2020 - 12:58:02 PM
Last modification on : Friday, August 5, 2022 - 2:54:00 PM
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  • HAL Id : hal-02507589, version 1



Huiwen Wang, Meiling Chen, Gilbert Saporta. Gram-Schmidt regression and application in cutting tool abrasion prediction. Journal of Beijing University of Aeronautics and Astronautics, Beijing University of Aeronautics and Astronautics, 2008, 34 (6), pp.729-733. ⟨hal-02507589⟩



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