An artificial neural network for predicting the friction coefficient of deposited Cr1?xAlxC films |
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Affiliation: | 1. School of Nursing, Third Military Medical University, 400038 Chongqing, China;2. Intensive Care Unit, The Affiliated Hospital of LuZhou Medical College, SiChuan 646000, China;3. School of Nursing, Chengdu Medical College, Chengdu, Sichuan, 618000, China |
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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 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. Compared to the conventional back propagation model, the GRNN model is more suitable to predict the friction coefficient of Cr1?xAlxC films. |
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