Abstract
The paper developed a system of tool wear monitoring in advanced manufacture systems. In conventional wear-monitoring method, it cannot exhibit unique behavior found in regular modern machining systems. Because a single monitoring signal, such as the signal of force, temperature, ultrasound or AE, cannot exactly describe the state of tool work for monitoring in advanced manufacture. This paper, therefore, mainly researched on real-time cutter state monitoring using neural network, neural network integration and multi-sensor information integrating technology. Picture pattern-recognition and feature extracting were adopted and combined with other information of the cutter dynamically. The characteristic information was gathered using an appropriate model of cutter wear or damage. Neural network were used to imitate the complicated nonlinear mapping relationship and to fuse multi-kind sensors that collect wearing and damage information and make decision and judgment rapidly.
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References
Nidal, H.A.Z., Yu, G.: Analytical Model for Tool Wear Monitoring in Turning Operations Using Ultrasound Waves. International Journal of Machine Tools & Manufacture 40, 1619–1635 (2000)
Scheffer, C., Kratz, H., Heyns, P.S., Klocke, F.: Development of A Tool Wear-Monitoring System for Hard Turning. International Journal of Machine Tools & Manufacture 43, 973–985 (2003)
Di, Y., Dany, E.W.T.I.: A Multi-sensor Strategy for Tool Failure Detection in Miling. Int. J. Mach. Tools Manufact. 35(3), 383–389 (1995)
Sick, B.: Online and Indirect Tool Wear Monitoring in Turning with Artificial Neural Networks: A Review of More Than a Decade of Research. Mechanical Systems and Signal Processing 16, 487–546 (2002)
Scheffer, C., Heyns, P.S.: Wear Monitoring in Turning Operations Using Vibration and Strain Measurements. Mechanical Systems and Signal Processing 15, 185–202 (2002)
Lee, B.Y., Liu, H.S., et al.: Monitoring of Tool Fracture in End Milling Using Induction Motor Current. Journal of Materials Processing Technology 70, 279–284 (1997)
Tansel, M.T., Nedbouyan, A.: Micro-end-milling—III. Wear Estimation and Tool Breakage Detection Using Acoustic Emission Signals. International Journal of Machine Tools & Manufacture 38, 1449–1466 (1998)
Ozel, T., Nadgir, A.: Prediction of Flank Wear by Using Back Propagation Neural Network Modeling When Cutting Hardened H-13 Steel with Chamfered and Honed CBN Tools. International Journal of Machine Tools & Manufacture 42, 287–297 (2002)
Kothamasu, R., Huang, S.H.: Intelligent Tool Wear Estimation for Hard Turning: Neural-Fuzzy Modeling and Model Evaluation. In: Proceedings of the Third International Conference on Intelligent Computation in Manufacturing Engineering, Ischia, Italy, pp. 343–346 (2002)
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Guo, L., Zhang, H., Qi, Y., Wei, Z. (2008). Study on Tool Wear Monitoring Based on Multi-source Information Fusion. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_14
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DOI: https://doi.org/10.1007/978-3-540-85984-0_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85983-3
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