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Process modeling and optimization using ANN and RSM during dry turning of titanium alloy used in automotive industry

Lightweight and high strength of titanium alloys makes them suitable for automotive industry. Automobiles are in high demand in the future and new materials and technologies are continuously enhancing the performance of automobiles. This research work addresses the potential use and barrier faced by the automobile industry in using titanium alloy. In this experimental study, we tried to overcome the machining challenges faced by industries to machine widely used grade–5 titanium alloy. The machining is performed under a dry machining environment due to ecological and economic concerns. The controllable machining variables and their influence on the tool wear are determined. Statistical analysis is performed to assess the impact of controllable variables. As a result, the optimal parameters setting obtained using the desirability approach, and confirmation runs carried out at optimal conditions. Moreover, a mathematical relation developed using the RSM technique to predict the response. Later on, using better modeling techniques based on artificial intelligence, a better predictive model is developed which forms the basis of cyber physical system.