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

Abstract

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.

Authors:
 [1];  [1];  [2];  [2]
  1. Louisiana State University
  2. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); High Temperature Materials Laboratory
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
931341
DOE Contract Number:  
DE-AC05-00OR22725
Resource Type:
Journal Article
Resource Relation:
Journal Name: International Journal of Machine Tools and Manufacture; Journal Volume: 47; Journal Issue: 3-4
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; ACCURACY; ACOUSTICS; ALGORITHMS; CREEP; DIAMONDS; GENETICS; GRINDING; MONITORING; REMOVAL; WHEELS

Citation Formats

Liao, T. W., Ting, C.F., Qu, Jun, and Blau, Peter Julian. A Wavelet-Based Methodology for Grinding Wheel Condition Monitoring. United States: N. p., 2007. Web. doi:10.1016/j.ijmachtools.2006.05.008.
Liao, T. W., Ting, C.F., Qu, Jun, & Blau, Peter Julian. A Wavelet-Based Methodology for Grinding Wheel Condition Monitoring. United States. doi:10.1016/j.ijmachtools.2006.05.008.
Liao, T. W., Ting, C.F., Qu, Jun, and Blau, Peter Julian. Mon . "A Wavelet-Based Methodology for Grinding Wheel Condition Monitoring". United States. doi:10.1016/j.ijmachtools.2006.05.008.
@article{osti_931341,
title = {A Wavelet-Based Methodology for Grinding Wheel Condition Monitoring},
author = {Liao, T. W. and Ting, C.F. and Qu, Jun and Blau, Peter Julian},
abstractNote = {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.},
doi = {10.1016/j.ijmachtools.2006.05.008},
journal = {International Journal of Machine Tools and Manufacture},
number = 3-4,
volume = 47,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2007},
month = {Mon Jan 01 00:00:00 EST 2007}
}