An artificial neural network for predicting the friction coefficient of deposited Cr1-xAlxC films

Authors:
Yu-Sen Yang;Jyh-Horng Chou;Wesley Huang;Tsow-Chang Fu;Guo-Wei Li
Affiliations:
Department of Mechanical and Automation Engineering, National Kaohsiung First University of Science and Technology, 1 University Road, Yenchao, Kaohsiung 824, Taiwan;Department of Mechanical and Automation Engineering, National Kaohsiung First University of Science and Technology, 1 University Road, Yenchao, Kaohsiung 824, Taiwan and Department of Electrical E ...;First Optotech Co., Ltd., 2 Juoyue Road, Nantz, Kaohsiung 811, Taiwan;Department of Mechanical and Automation Engineering, National Kaohsiung First University of Science and Technology, 1 University Road, Yenchao, Kaohsiung 824, Taiwan;Department of Mechanical and Automation Engineering, National Kaohsiung First University of Science and Technology, 1 University Road, Yenchao, Kaohsiung 824, Taiwan
Venue:
Applied Soft Computing
Year:
2013

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Abstract

This paper applies a generalized regression neural network (GRNN) for predicting the friction coefficient of deposited Cr"1"-"xAl"xC films on high-speed steel substrates via direct current magnetron sputtering systems. The Cr"1"-"xAl"xC films exhibited some excellent characteristics, such as low friction coefficient, high hardness, and large contact angle. In this study, a GRNN model is applied for predicting the friction coefficient of Cr"1"-"xAl"xC films on high-speed steel substrates instead of complex practical experiments. The results exhibit good prediction accuracy of friction coefficient since about +/-0.97% average errors and show the feasibility of the prediction model. Compared to the conventional back propagation model, the GRNN model is more suitable to predict the friction coefficient of Cr"1"-"xAl"xC films.