Optimization of Surface Roughness and Tool Life for Turning S45C Steel Using Response Surface Methodology

Mechanical Engineering

Response surface methodology is successfully applied in optimizing the tool life and surface roughness for the chosen tool work combination and for the selected domain of the input machining parameters. The work develops a predictive and optimization model by coupling the two approaches, artificial neural network and response surface methodology. Surface roughness, an indicator of surface quality is one of the most specified customer requirements in a machining process. For efficient application of machine tools, optimum cutting parameters (speed, feed and depth of cut) are required. Therefore, it is necessary to determine a suitable optimization method which can find optimum values of cutting parameters for minimizing surface roughness and improve the tool life. The turning process parameter optimization is highly constrained and nonlinear. In present work, machining process was carried out on S45C steel material in dry cutting condition in a lathe machine. To predict the surface roughness and tool life, an artificial neural network model was designed through back propagation network for the data obtained. The predicted data was designed by using central composite design and analysed in Minitab 17.0 and compared the optimized result in good literature. The results obtained, conclude that RSM is reliable and accurate for solving the cutting parameter optimization.

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