Document Type
Conference Proceeding
Publication Date
8-17-2023
Department
Department of Mechanical Engineering-Engineering Mechanics
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
Publication Title
Machine Learning
Recommended Citation
Sotubadi, S.,
Liu, R.,
&
Nguyen, V.
(2023).
Explainable AI for tool wear prediction in turning.
Machine Learning.
http://doi.org/10.48550/arXiv.2308.08765
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/807
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Version
Publisher's PDF