Comparative analysis of different machine vision algorithms for tool wear measurement during machining
Author:
Funder
Nirma University
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10845-024-02467-3.pdf
Reference59 articles.
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2. Altin, A., Nalbant, M., & Taskesen, A. (2007). The effects of cutting speed on tool wear and tool life when machining Inconel 718 with ceramic tools. Materials and Design, 28(9), 2518–2522. https://doi.org/10.1016/j.matdes.2006.09.004
3. Balochian, S., & Baloochian, H. (2022). Edge detection on noisy images using Prewitt operator and fractional order differentiation. Multimedia Tools and Applications, 81(7), 9759–9770. https://doi.org/10.1007/s11042-022-12011-1
4. Balu, A., Sarkar, S., Ganapathysubramanian, B., & Krishnamurthy, A. (2022). Physics-aware machine learning surrogates for real-time manufacturing digital twin. Manufacturing Letters, 34, 71–74. https://doi.org/10.1016/j.mfglet.2022.08.013
5. Banda, T., Jauw, V. L., Farid, A. A., Wen, N. H., Xuan, K. C. W., & Lim, C. S. (2023). In-process detection of failure modes using YOLOv3-based on-machine vision system in face milling Inconel 718. The International Journal of Advanced Manufacturing Technology, 128(9–10), 3885–3899. https://doi.org/10.1007/s00170-023-12168-5
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