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Trouble diagnosis of the grinding process by using acoustic emission signals

✍ Scribed by Jae-Seob Kwak; Ji-Bok Song

Publisher
Elsevier Science
Year
2001
Tongue
English
Weight
865 KB
Volume
41
Category
Article
ISSN
0890-6955

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✦ Synopsis

The focus of this study is the development of a credible diagnosis system for the grinding process. The acoustic emission signals generated during machining were analyzed to determine the relationship between grinding-related troubles 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 (m), a learning rate (a), and a structure of the hidden layer in the iterative learning process. The success rates of trouble recognition were verified.

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