Application of wavelet analysis in tool wear evaluation using image processing method
Tool wear plays a significant role for proper planning and control of machining parameters to maintain the product quality. However, existing tool wear monitoring methods using sensor signals still have limitations. Since the cutting tool operates directly on the work-piece during machining process,...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Published: |
Science Publishing Corporation
2018
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/4611/ https://dx.doi.org/ 10.14419/ijet.v7i4.36.28155 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833417644231884800 |
|---|---|
| author | Lee, Woon Kiow Tuan Muda, Syed Mohamad Aiman Ong, Pauline Sia, Chee Kiong Talib, Norfazillah Saleh, Aslinda |
| author_facet | Lee, Woon Kiow Tuan Muda, Syed Mohamad Aiman Ong, Pauline Sia, Chee Kiong Talib, Norfazillah Saleh, Aslinda |
| author_sort | Lee, Woon Kiow |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Tool wear plays a significant role for proper planning and control of machining parameters to maintain the product quality. However, existing tool wear monitoring methods using sensor signals still have limitations. Since the cutting tool operates directly on the work-piece during machining process, the machined surface provides valuable information about the cutting tool condition. Therefore, the objective of present study is to evaluate the tool wear based on the workpiece profile signature by using wavelet analysis. The effect of wavelet families, scale of wavelet and statistical features of the continuous wavelet coefficient on the tool wear is studied. The surface profile of workpiece was captured using a DSLR camera. Invariant moment method was applied to extract the surface profile up to sub-pixel accuracy. The extracted surface profile was analyzed by using continuous wavelet transform (CWT) written in MATLAB. The re-sults showed that average, RMS and peak to valley of CWT coefficients at all scale increased with tool wear. Peak to valley at higher scale is more sensitive to tool wear. Haar was found to be more effective and significant to correlate with tool wear with highest R2 which is 0.9301. |
| format | Article |
| id | my.uthm.eprints-4611 |
| institution | Universiti Tun Hussein Onn Malaysia |
| publishDate | 2018 |
| publisher | Science Publishing Corporation |
| record_format | eprints |
| spelling | my.uthm.eprints-46112021-12-07T08:58:26Z http://eprints.uthm.edu.my/4611/ Application of wavelet analysis in tool wear evaluation using image processing method Lee, Woon Kiow Tuan Muda, Syed Mohamad Aiman Ong, Pauline Sia, Chee Kiong Talib, Norfazillah Saleh, Aslinda TJ1125-1345 Machine shops and machine shop practice Tool wear plays a significant role for proper planning and control of machining parameters to maintain the product quality. However, existing tool wear monitoring methods using sensor signals still have limitations. Since the cutting tool operates directly on the work-piece during machining process, the machined surface provides valuable information about the cutting tool condition. Therefore, the objective of present study is to evaluate the tool wear based on the workpiece profile signature by using wavelet analysis. The effect of wavelet families, scale of wavelet and statistical features of the continuous wavelet coefficient on the tool wear is studied. The surface profile of workpiece was captured using a DSLR camera. Invariant moment method was applied to extract the surface profile up to sub-pixel accuracy. The extracted surface profile was analyzed by using continuous wavelet transform (CWT) written in MATLAB. The re-sults showed that average, RMS and peak to valley of CWT coefficients at all scale increased with tool wear. Peak to valley at higher scale is more sensitive to tool wear. Haar was found to be more effective and significant to correlate with tool wear with highest R2 which is 0.9301. Science Publishing Corporation 2018 Article PeerReviewed Lee, Woon Kiow and Tuan Muda, Syed Mohamad Aiman and Ong, Pauline and Sia, Chee Kiong and Talib, Norfazillah and Saleh, Aslinda (2018) Application of wavelet analysis in tool wear evaluation using image processing method. International Journal of Engineering and Technology, 7 (4.36). pp. 426-431. ISSN 2227-524X https://dx.doi.org/ 10.14419/ijet.v7i4.36.28155 |
| spellingShingle | TJ1125-1345 Machine shops and machine shop practice Lee, Woon Kiow Tuan Muda, Syed Mohamad Aiman Ong, Pauline Sia, Chee Kiong Talib, Norfazillah Saleh, Aslinda Application of wavelet analysis in tool wear evaluation using image processing method |
| title | Application of wavelet analysis in tool wear evaluation using image processing method |
| title_full | Application of wavelet analysis in tool wear evaluation using image processing method |
| title_fullStr | Application of wavelet analysis in tool wear evaluation using image processing method |
| title_full_unstemmed | Application of wavelet analysis in tool wear evaluation using image processing method |
| title_short | Application of wavelet analysis in tool wear evaluation using image processing method |
| title_sort | application of wavelet analysis in tool wear evaluation using image processing method |
| topic | TJ1125-1345 Machine shops and machine shop practice |
| url | http://eprints.uthm.edu.my/4611/ https://dx.doi.org/ 10.14419/ijet.v7i4.36.28155 |
| url_provider | http://eprints.uthm.edu.my/ |