Journal Press India^®

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Neural Networks are information processing systems and can be used in several areas of engineering applications and eliminate limitations of the classical approaches by extracting the desired information using the input data. The advantage of the usage of neural networks for prediction is that they are able to learn from examples only and that after their learning is finished, they are able to catch hidden and strongly nonlinear dependencies, even when there is significant noise in the training set. One of the most specified customer requirements in a machining process is surface roughness. For efficient use of machine tools, optimum cutting parameters are required. Therefore it is necessary to find a suitable optimization method which can find optimum values of cutting parameters for minimizing surface roughness. The turning process parameter optimization is highly constrained and nonlinear. Many researchers have used an artificial neural network (ANN) model for the data obtained through experiments to predict the surface roughness. The results obtained, conclude that ANN is reliable and accurate for solving the cutting parameter optimization. The paper work presents on all studies where ANN has been used to analyse surface roughness in turning process.

Keywords

Surface Roughness; Turning; Artificial Neural Network; Parametric Analysis

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