Journal
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume 62, Issue 1-4, Pages 107-121Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00170-011-3797-1
Keywords
Grinding wheel wear; Acoustic emission (AE); Process monitoring; Contact detection; Discrete wavelet decomposition; Support vector machine (SVM)
Funding
- National Natural Science Foundation of China [71071138, 50835008]
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A novel grinding wheel wear monitoring system based on discrete wavelet decomposition and support vector machine is proposed. The grinding signals are collected by an acoustic emission (AE) sensor. A preprocessing method is presented to identify the grinding period signals from raw AE signals. Root mean square and variance of each decomposition level are designated as the feature vector using discrete wavelet decomposition. Various grinding experiments were performed on a surface grinder to validate the proposed classification system. The results indicate that the proposed monitoring system could achieve a classification accuracy of 99.39% with a cut depth of 10 mu m, and 100% with a cut depth of 20 mu m. Finally, several factors that may affect the classification results were discussed as well.
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