Identifying Tool Wear Stages in Turning Process through Machined Surface Image Analysis Using Convolutional Neural Network

Implementing tool wear monitoring approaches is often challenging due to the requirements of directly observing the wear state or integrating sensor-based instrumentation. This work proposes identifying wear stages of a turning tool by analyzing the machined surface quality. An indirect tool wear classification approach is presented to categorize tool wear into three classes - initial wear, steady wear stages, and catastrophic wear during the turning operation. The machined surface images were captured over diverse process parameters to realize labeled datasets for these three wear classes. A pre-trained Convolutional Neural Network (CNN), EfficientNet-b0, was fine-tuned using transfer learning to classify the surface images and predict tool wear stages subsequently. The proposed approach demonstrated the potential to offer an alternative solution to on-machine tool wear monitoring. Although the primary results showed the utility of the proposed approach in predicting tool wear stages, the analysis of misclassifications using confidence scores and heatmaps revealed some discrepancies. It highlighted the need for further research to enhance surface image features that can realize a robust and reliable indirect tool wear classification model.