Abstract
The focus of this study is the development of a credible fault detection system of the cylindrical plunge grinding process. The acoustic emission (AE) signals generated during machining were analyzed to determine the relationship between grinding-related faults and characteristics of changes in signals. Furthermore, a neural network, which has excellent ability in pattern classification, was applied to the diagnosis system. The neural network was optimized with a momentum coefficient, a learning rate, and a structure of the hidden layer in the iterative learning process. The success rates of fault detection were verified.
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Kwak, JS., Song, JB. Fault detection of the cylindrical plunge grinding process by using the parameters of AE signals. KSME International Journal 14, 773–781 (2000). https://doi.org/10.1007/BF03184463
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DOI: https://doi.org/10.1007/BF03184463