Tool Wear and Fault Prediction Systems Powered by AI

M. Amareswara Kumar, G. Jayasai Karthik, D. Hussain Basha, S. Ashraf, P. Ramesh, O. Yogeeswar

2025

Abstract

Tool wear and fault prediction to preserving the efficiency and productivity of the manufacturing process, which ensure the quality of the product and reduce downtime. In recent years, the progress of Artificial Intelligence (AI) has exposed new possibilities to develop future systems that can autonomously monitor and analyse machine conditions. The project proposes the development of a tool wear and fault prediction systems powered by AI, which takes advantage of the leveraging machine learning algorithm in real time to decline and predict potential defects. During the system operation, to catch the dynamic behaviour of the tools will collect data from various sensors embedded in the machinery, such as vibration, cutting force, temperature, current and acoustic sensors, such as the dynamic behaviour of the tools. Using AI techniques, especially supervised learning models such as neural networks and support vector machine (SVM) are monitored, the system will be trained to identify patterns and correlations between sensor data and tool wear or fault position.

Paper Citation

in Harvard Style

Kumar M., Karthik G., Basha D., Ashraf S., Ramesh P. and Yogeeswar O. (2025). Tool Wear and Fault Prediction Systems Powered by AI. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 226-231. DOI: 10.5220/0013880500004919

in Bibtex Style

@conference{icrdicct`2525,
author={M. Kumar and G. Karthik and D. Basha and S. Ashraf and P. Ramesh and O. Yogeeswar},
title={Tool Wear and Fault Prediction Systems Powered by AI},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={226-231},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013880500004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Tool Wear and Fault Prediction Systems Powered by AI
SN - 978-989-758-777-1
AU - Kumar M.
AU - Karthik G.
AU - Basha D.
AU - Ashraf S.
AU - Ramesh P.
AU - Yogeeswar O.
PY - 2025
SP - 226
EP - 231
DO - 10.5220/0013880500004919
PB - SciTePress