Keywords
Abstract
Tool wear significantly affects machining efficiency, leading to increased costs, reduced precision, and frequent maintenance. The integration of advanced monitoring techniques and predictive maintenance strategies has revolutionized tool wear management, enabling real-time analysis and proactive interventions. This paper explores various methodologies, including machine learning models, sensor fusion, and adaptive control systems, to optimize tool life and machining efficiency. The challenges associated with data-driven approaches, implementation in diverse industrial environments, and the scalability of adaptive control systems are also discussed.
The study highlights the need for continued research in integrating artificial intelligence (AI) and real-time monitoring to enhance predictive capabilities and improve machining processes.
References
Astakhov, V. P. (2004). The assessment of cutting tool wear. International journal of machine tools and manufacture, 44(6), 637-647.
Taherdoost, H., & Madanchian, M. (2023). AI advancements: Comparison of innovative techniques. AI, 5(1), 38-54.
Calin, O. (2020). Deep learning architectures. New York City: Springer International Publishing.
Putka, D. J., Beatty, A. S., & Reeder, M. C. (2018). Modern prediction methods: New perspectives on a common problem. Organizational Research Methods, 21(3), 689-732.
Brox, T., Bruhn, A., Papenberg, N., & Weickert, J. (2004). High accuracy optical flow estimation based on a theory for warping. In Computer Vision-ECCV 2004: 8th European Conference on Computer Vision, Prague, Czech Republic, May 11-14, 2004. Proceedings, Part IV 8 (pp. 25-36). Springer Berlin Heidelberg.
National Research Council, Global Affairs, Technology for Sustainability Program, & Committee on Incorporating Sustainability in the US Environmental Protection Agency. (2011). Sustainability and the US EPA. National Academies Press.
Tufano, M., Agarwal, A., Jang, J., Moghaddam, R. Z., & Sundaresan, N. (2024). AutoDev: Automated AI-driven development. arXiv preprint arXiv:2403.08299.