Title :
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
Abstract :
This paper applies a generalized regression neural network (GRNN) for predicting the friction coefficient of deposited Cr1-xAlxC films on high-speed steel substrates via direct current (DC) magnetron sputtering systems. The Cr1-xAlxC 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 Cr1-xAlxC 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;
Conference_Titel :
System Science and Engineering (ICSSE), 2010 International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-6472-2
DOI :
10.1109/ICSSE.2010.5551732
