Friction coefficient prediction of deposited Cr1−xAlxC coatings using neural networks

Author

Yang, Yu-Sen ; Fu, Tsow-Chang ; Chou, Jyh-Hong ; Huang, Wesley ; Li, Guo-Wei

Author_Institution

Dept. of Mech. & Autom. Eng., Nat. Kaohsiung First Univ. of Sci. & Technol., Kaohsiung, Taiwan

fYear

2010

Firstpage

46

Lastpage

49

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 (DC) 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.

Keywords

chromium compounds; friction; neural nets; regression analysis; sputtered coatings; steel; steel industry; direct current magnetron sputtering systems; friction coefficient prediction; generalized regression neural network; high-speed steel substrates; Friction; Predictive models; Unbalanced magnetron sputtering; chromium aluminum carbides; generalized regression neural network; prediction model;

fLanguage

English

Publisher

ieee

Conference_Titel

System Science and Engineering (ICSSE), 2010 International Conference on

Conference_Location

Taipei

Print_ISBN

978-1-4244-6472-2

Type

conf

DOI

10.1109/ICSSE.2010.5551732

Filename

5551732