Multi-Objective Optimization of Cutting Parameters in Hard Turning Process Using Genetic Algorithm (GA) & Artificial Neural Network(ANN)

Manufacturing industry today faces the challenge of having to develop high quality products faster and economically than ever before. Therefore optimization is seen as an innovative technique under certain premises. Optimization in turning means determination of the optimal set of machining parameters to satisfy the objectives within the operational constraints. Predictive modeling is essential for understanding and optimization of the machining process. The aim of this study is to develop an integrated model to optimize the cutting parameters that are affecting the quality of surface produced in hard turning process on EN 353 metal. Three input parameters were selected for study: cutting speed, feed & depth of cut to determine the optimal cutting parameters required for minimum surface roughness, power consumption and for maximum metal removal rate. Mathematical equations are formulated as objective functions, to determine the optimal cutting parameters so that minimization of surface roughness/power consumption and maximization of metal removal rate are evaluated by using MATLAB software. Genetic Algorithm(GA) supported with tested ANN is utilized to determine the best combinations of cutting parameters through optimization process. Artificial Neural Network (ANN) on back propagation learning with hidden neurons is used to validate the model The trained machined data was tested and the results show that the model has the ability to solve many problems including predicting, modeling and measuring experimental knowledge under dry environment. From these results, it can be easily realized that the developed study is reliable and suitable for solving the other parameters encountered in metal cutting operations as the same as surface roughness.