  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.
