Optimization of physical vapour deposition coating process parameters using genetic algorithm
Optimization of thin film coating parameter is an important task to identify the required output. In the process of physical vapor deposition (PVD), two main issues of the PVD process are cost of manufacturing and customization of the cutting tool properties. In general, a proper choice of the coati...
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| 總結: | Optimization of thin film coating parameter is an important task to identify the required output. In the process of physical vapor deposition (PVD), two main issues of the PVD process are cost of manufacturing and customization of the cutting tool properties. In general, a proper choice of the coating process parameters is very important to find the best characteristics of coating and towards less material usage, reduced trial in experiment and less machine maintenance. The aim of this study is to identify optimal PVD coating process parameters. Three process parameters were selected which are nitrogen gas pressure (N2), argon gas pressure (Ar) and turntable speed (TT), while thin film grain size of titanium nitrite (TiN) was selected as an output response. In order to get output result, the three parameters were used to develop a polynomial quadratic equation that was designed using response surface methodology (RSM). Then, in order to optimize the coating process parameters, genetic algorithms (GAs) were used for the optimization work. The results showed that the optimized coating process parameters have lower grain size value compared to the actual experimental data and RSM with (≈6%) ratio and (≈0.03%) ratio, respectively. |
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