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Title: A Wavelet-Based Methodology for Grinding Wheel Condition Monitoring

Journal Article · · International Journal of Machine Tools and Manufacture
 [1];  [1];  [2];  [2]
  1. Louisiana State University
  2. ORNL
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Grinding wheel surface condition changes as more material is removed. This paper presents a wavelet-based methodology for grinding wheel condition monitoring based on acoustic emission (AE) signals. Grinding experiments in creep feed mode were conducted to grind alumina specimens with a resinoid-bonded diamond wheel using two different conditions. During the experiments, AE signals were collected when the wheel was 'sharp' and when the wheel was 'dull'. Discriminant features were then extracted from each raw AE signal segment using the discrete wavelet decomposition procedure. An adaptive genetic clustering algorithm was finally applied to the extracted features in order to distinguish different states of grinding wheel condition. The test results indicate that the proposed methodology can achieve 97% clustering accuracy for the high material removal rate condition, 86.7% for the low material removal rate condition, and 76.7% for the combined grinding conditions if the base wavelet, the decomposition level, and the GA parameters are properly selected.

Research Organization:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). High Temperature Materials Lab. (HTML)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
DOE Contract Number:
DE-AC05-00OR22725
OSTI ID:
931341
Journal Information:
International Journal of Machine Tools and Manufacture, Vol. 47, Issue 3-4; ISSN 0890-6955
Publisher:
Elsevier
Country of Publication:
United States
Language:
English

References (17)

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
ACCURACY
ACOUSTICS
ALGORITHMS
CREEP
DIAMONDS
GENETICS
GRINDING
MONITORING
REMOVAL
WHEELS