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

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.

► In this study, we propose the estimation of hardness of titanium thin-film layers as protective industrial tools. ► Neural networks are used to estimate the maximum hardness of titanium thin film. ► We apply the statistical approach to optimize the coating parameters in coating of TiC, TiN, and TiC-N materials. ► We conclude that optimal coating parameters can provide the maximum hardness of coated layers among the initial tests.