Optimisation of turning parameters by integrating genetic algorithm with support vector regression and artificial neural networks

Abstract

This paper focuses on optimisation of process parameters of the turning operation, using artificial intelligence techniques such as support vector regression (SVR) and artificial neural networks (ANN) integrated with genetic algorithm (GA). The model is trained using the turning parameters as the input and corresponding surface roughness, tool wear and power required as the output. Data, obtained from conducting experiments is analysed using support vector machine (SVM) and artificial neural network. SVM, a nonlinear model, is learned by linear learning machine by mapping into high-dimensional kernel-induced feature space. The genetic algorithm is integrated with these to find the optimum from the response surface generated. The results are compared with those obtained by integrating GA with traditional models like response surface methodology (RSM) and regression analysis (RA). This paper illustrates the impact that techniques based on artificial intelligence have on optimising processes.