Noise robust classification of carbide tool wear in machining mild steel using texture extraction based transfer learning approach for predictive maintenance

Acoustics based smart condition monitoring is a viable alternative to mechanical vibrations or image-capture based predictive maintenance methods. In this study, a texture analysis based transfer learning methodology was applied to classify tool wear based on the noise generated during mild steel machining. The machining acoustics were converted to spectrogram images and transfer learning was applied for their classification into high/medium/low tool wear using four pre-trained deep learning models (SqueezeNet, ResNet50, InceptionV3, GoogLeNet). Moreover, three optimizers (RMSPROP, ADAM, SGDM) were applied to each of the deep learning models to enhance classification accuracies. Primary results indicate that the InceptionV3-RMSPROP obtained the highest testing accuracy of 87.50%, followed by the SqueezeNet-RMSPROP and ResNet50-SGDM at 75.00% and 62.50% respectively. However, SqueezeNet-RMSPROP was determined to be more desirable from a practical machining quality and safety perspective, owing to its greater recall value for the highest tool wear class. The proposed acoustics-texture extraction-transfer learning approach is especially suitable for cost effective tool wear condition monitoring involving limited datasets.