Abstract
This investigation has designed a tool condition monitoring system (TCM) while milling of Inconel 625 based on sound and vibration signatures. The experiments were carried out based on response surface methodology (RSM) central composite design, design of experiments. The process parameters such as speed, feed, depth of cut and vegetable-based cutting fluids were optimized based on surface roughness, flank wear. It was found that the sound pressure and vibration signatures have the direct relation with flank wear. The statistical features like root mean square, skewness, kurtosis and mean values were extracted from the experimental data. From the designed NN estimator, the cutting tool flank wear was predicted with the mean square error (MSE) of 0.084212.
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