Journal Press India^®

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Surface roughness, is the most specified customer requirements in a machining process. For efficient use of machine tools, optimum cutting parameters (speed, feed and depth of cut) are required. Therefore it is necessary to find a suitable optimization method which can find optimum values of cutting parameters for minimizing surface roughness. To predict the surface roughness many researchers used artificial neural network model. Comparison of the experimental data and neural network model results has shown that there is no significant difference and neural network model was used confidently. This paper deals with review study of works using Artificial Neural Networks ANN, in predicting the surface roughness in turning process. Some of the machining variables that have a major impact on the surface roughness in turning process such as spindle speed, feed rate and depth of cut were considered as inputs and surface roughness as output. The predicted surface roughness values computed from ANN, are compared with experimental data and the results obtained, conclude that neural network model is reliable and accurate for solving the cutting parameter optimization.

Keywords

Artificial Neural Network; Surface Roughness; Turning; Cutting Parameters; Optimization

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