Thin-film coating plays a prominent role on the manufacture of many industrial devices. Coating can increase material performance due to the deposition process. Having adequate and precise model that can predict the hardness of PVD and CVD processes is so helpful for manufacturers and engineers to choose suitable parameters in order to obtain the best hardness and decreasing cost and time of industrial productions. 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 that was obtained during the process of chemical vapor deposition (CVD) and physical vapor deposition (PVD), the modeling of the coating variables for predicting hardness of titanium thin-film layers, is performed. Then, the obtained results are experimentally verified and very accurate outcomes had been attained.
Oxidation of boron-titanium thin film coating during cyclic tests on thermal shock
