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

This paper applies a generalized regression neural network (GRNN) for predicting the friction coefficient of deposited Cr
_1-x
Al
_x
C films on high-speed steel substrates via direct current (DC) magnetron sputtering systems. The Cr
_1-x
Al
_x
C 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-x
Al
_x
C 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.