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.
| Original language | English |
|---|---|
| Pages (from-to) | 580-592 |
| Number of pages | 13 |
| Journal | International Journal of Machine Tools and Manufacture |
| Volume | 47 |
| Issue number | 3-4 |
| DOIs | |
| State | Published - Mar 2007 |
Funding
This research was made possible with the support provided by two programs: (i) the High Temperature Materials Laboratory User Program, Oak Ridge National Laboratory sponsored by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Transportation Technologies, and (ii) the Louisiana Board's NSF ESPCoR Links Program with Contract no. NSF(2005)-LINK-06.
Keywords
- Acoustic emission
- Adaptive genetic algorithm
- Clustering
- Condition monitoring
- Discrete wavelet decomposition
- Grinding wheel wear