International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Tool Wear and Fault Diagnosis and Prognostics Powered by AI

M. Amareswara KumarAssistant Professor, Department of Computer Science & Engineering, Santhiram Engineering College, Nandyal, A.P., IndiaG. Jayasai KarthikUG Student, Department of Computer Science & Engineering, Santhiram Engineering College, Nandyal, A.P., IndiaD. Hussain BashaUG Student, Department of Computer Science & Engineering, Santhiram Engineering College, Nandyal, A.P., IndiaS. AshrafUG Student, Department of Computer Science & Engineering, Santhiram Engineering College, Nandyal, A.P., IndiaP. RameshUG Student, Department of Computer Science & Engineering, Santhiram Engineering College, Nandyal, A.P., IndiaO. YogeeswarUG Student, Department of Computer Science & Engineering, Santhiram Engineering College, Nandyal, A.P., IndiaK. ChaitanyaUG Student, Department of Computer Science & Engineering, Santhiram Engineering College, Nandyal, A.P., India

Vol 9 No 25 (2025): Volume 9, Special Issue of INSPIRE’25 April 2025 | Pages: 349-354

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 24-04-2025

doi Logo doi.org/10.47001/IRJIET/2025.INSPIRE56

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Abstract

Tool wear and fault detection are paramount to manufacturing operation effectiveness and dependability. The project suggests an artificial intelligence system for real-time tool wear prediction and fault prognostics using unsupervised learning and advanced data-driven methods. The system monitors data from sensors, including vibration, temperature, and acoustic emissions, embedded within the machinery. Clustering algorithms, anomaly detection algorithms, and dimensionality reduction algorithms (such as K-means, DBSCAN, and PCA) are applied to detect patterns and anomalies in tool behaviour in the absence of label data. Reinforcement learning (RL) algorithms are applied to optimize maintenance policies by learning machine interactions continuously. This autonomous mechanism facilitates early fault detection, minimizes surprise failures, and maximizes overall manufacturing productivity and cost-effectiveness.

Keywords

Principal Component Analysis, Clustering algorithms, reinforcement learning algorithms, K-means clustering, dimensionality reduction


Citation of this Article

M. Amareswara Kumar, G. Jayasai Karthik, D. Hussain Basha, S. Ashraf, P. Ramesh, O. Yogeeswar, & K. Chaitanya. (2025). Tool Wear and Fault Diagnosis and Prognostics Powered by AI. In proceeding of International Conference on Sustainable Practices and Innovations in Research and Engineering (INSPIRE'25), published by IRJIET, Volume 9, Special Issue of INSPIRE’25, pp 349-354. Article DOI https://doi.org/10.47001/IRJIET/2025.INSPIRE56

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