Improving vision-based tool wear state identification under varying lighting conditions using human guided-explainable AI approach

Abstract

Vision-based tool wear monitoring systems augmented with Artificial Intelligence (AI)-based algorithms can effectively identify tool wear states. However, inconsistent image quality due to varying lighting conditions on manufacturing shop floors often obscures the scalability and reliability of these systems for practical applications. This study presents an on-machine vision-based tool wear monitoring system capable of handling varying lighting conditions using human guidance and an eXplainable AI (XAI) approach. The present study captured tool wear images under two lighting conditions, L1 and L2, using a microscope-based on-machine image acquisition system. The images were classified into four tool wear states: Flank, Flank + BUE, Flank + Face, and Chipping, commonly observed while machining Inconel 718 (IN718). Tool wear images captured under the L1 lighting condition were used to train the Convolutional Neural Network-based Efficient-Net-b0 model. The model was integrated subsequently with the Human Guided-XAI (HG-XAI) approach to predict tool wear states for images captured under the L2 lighting condition. The performance of the HG-XAI approach was evaluated using metrics such as Accuracy, Matthews Correlation Coefficient (MCC), and F1-Score and compared with the standalone Efficient-Net-b0 model. The results show that the HG-XAI approach achieved an accuracy of 96%, MCC of 0.96, and F1-Score of 0.97, demonstrating significant improvements over the standalone Efficient-Net-b0 model. The findings of this paper substantiate the scalability and reliability of the integrated HG-XAI approach under varying lighting conditions.