Explainable AI for tool wear prediction in turning

TL;DR

This paper develops an explainable AI framework using random forests and Shapley values to predict tool wear in turning, providing insights into feature importance for better human understanding.

Contribution

It introduces an XAI approach with Shapley values for interpreting ML-based tool wear predictions in turning processes.

Findings

Tool temperature is the most significant feature for prediction.

XAI enables operators to understand ML decision-making.

Random forest effectively predicts tool condition.

Abstract

This research aims develop an Explainable Artificial Intelligence (XAI) framework to facilitate human-understandable solutions for tool wear prediction during turning. A random forest algorithm was used as the supervised Machine Learning (ML) classifier for training and binary classification using acceleration, acoustics, temperature, and spindle speed during the orthogonal tube turning process as input features. The ML classifier was used to predict the condition of the tool after the cutting process, which was determined in a binary class form indicating if the cutting tool was available or failed. After the training process, the Shapley criterion was used to explain the predictions of the trained ML classifier. Specifically, the significance of each input feature in the decision-making and classification was identified to explain the reasoning of the ML classifier predictions. After implementing the Shapley criterion on all testing datasets, the tool temperature was identified as the most significant feature in determining the classification of available versus failed cutting tools. Hence, this research demonstrates capability of XAI to provide machining operators the ability to diagnose and understand complex ML classifiers in prediction of tool wear.