In the present work, a back propagation neural network model has been developed for the prediction of flank wear in turning operations. Large numbers of experiments have been performed on mild steel work-piece material using high speed steel (HSS) as the cutting tool. Process parametric conditions including cutting speed, feed-rate, depth of cut, and the measured parameters such as cutting force, chip thickness and vibration signals are used as inputs to the neural network model. Flank wear of the cutting tool corresponding to these conditions is the output of the neural network. The inclusion of chip width in addition to the existing inputs to the neural network model has been considered. The convergence of mean square error both in training and testing came out very well. The performance of the trained neural network has been tested with the experimental data, and is found to be in good agreement.

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