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.

该研究旨在开发一种可解释的人工智能（XAI）框架，以实现在车削过程中对工具磨损进行可理解的预测。通过使用随机森林算法作为监督式机器学习（ML）分类器，以加速度、声学、温度和主轴转速等输入特征进行二元分类训练，该分类器可用于预测切割过程后刀具的状态，以表示切割刀具是否可用或失效。在训练过程中，使用了 Shapley 准则来解释训练后的 ML 分类器的预测。具体而言，识别了决策和分类中每个输入特征的重要性，以解释 ML 分类器预测的推理。在对所有测试数据集实施 Shapley 准则后，发现刀具温度是确定可用和失效切割刀具分类的最重要特征。因此，该研究展示了 XAI 在工具磨损预测中为加工操作者提供诊断和理解复杂的 ML 分类器的能力。