Optimization of coating variables for hardness of industrial tools by using artificial neural networks

摘要

Thin-film coating plays a prominent role on the manufacture of many industrial devices. Coating can increase material performance due to the deposition process. This paper proposes the estimation of hardness of titanium thin-film layers as protective industrial tools by using multi layer perceptron (MLP) neural network. Based on the experimental data obtained during the process of chemical vapor deposition (CVD) and physical vapor deposition (PVD), the optimization of the coating variables for achieving the maximum hardness of titanium thin-film layers, is performed. Then, the obtained results are experimentally verified. During titanium coating, improvements of up to 16.75% of the layers hardness are accessible.