HG-XAI: human-guided tool wear identification approach through augmentation of explainable artificial intelligence with machine vision

Aitha Sudheer Kumar (), Ankit Agarwal (), Vinita Gangaram Jansari (), K. A. Desai (), Chiranjoy Chattopadhyay () and Laine Mears ()
Additional contact information
Aitha Sudheer Kumar: Indian Institute of Technology Jodhpur
Ankit Agarwal: Clemson University
Vinita Gangaram Jansari: Clemson University
K. A. Desai: Indian Institute of Technology Jodhpur
Chiranjoy Chattopadhyay: FLAME University
Laine Mears: Clemson University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 7, No 17, 4807-4822

Abstract: Abstract Identifying tool wear state is essential for machine operators as it assists in informed decisions for timely tool replacement and subsequent machining operations. As each wear state corresponds to a unique mitigation strategy, timely identification is vital while implementing solutions to minimize tool wear. The paper presents a novel Human Guided-eXplainable Artificial Intelligence (HG-XAI) approach for identifying the tool wear state by integrating human intelligence and eXplainable AI with a pre-trained Convolutional Neural Network (CNN), Efficient-Net-b0 model. The tool wear states were identified based on different wear mechanisms during the machining of IN718. The study considers four distinct tool wear states, i.e., Flank, Flank+BUE, Flank+Face, and Chipping, representing abrasion, adhesion, diffusion, and fracture wear mechanisms. The image-based datasets were created to depict various tool wear states by machining IN718 at varying surface speeds. The effectiveness of the proposed HG-XAI approach was evaluated by comparing its prediction accuracy with a standalone Efficient-Net-b0 model lacking human intelligence and XAI. Further, the scalability of the HG-XAI approach was examined by predicting wear states from images acquired at different cutting parameters. The results from the present study showed that the HG-XAI approach can predict the tool wear state with an accuracy of 93.08% and is scalable to variations in cutting conditions. Also, the proposed approach can be extended while developing vision-based on-machine tool wear monitoring systems.

Keywords: Tool wear; Convolutional Neural Networks (CNNs); Explainability; Grad-CAM; Human intelligence (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-024-02476-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:36:y:2025:i:7:d:10.1007_s10845-024-02476-2

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-024-02476-2

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().