Machining process classification using PCA reduced histogram features and the support vector machine
Being able to identify machining processes that produce specific machined surfaces is crucial in modern manufacturing production. Image processing and computer vision technologies have become indispensable tools for automated identification with benefits such as reduction in inspection time and avoi...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/48220/1/Machining%20process%20classification%20using%20PCA%20reduced%20histogram%20features%20and%20the%20support%20vector%20machine.pdf |
| _version_ | 1796974949391400960 |
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| author | Ashour, Mohammed Waleed Khalid, Fatimah Abdul Halin, Alfian Abdullah, Lili Nurliyana |
| author_facet | Ashour, Mohammed Waleed Khalid, Fatimah Abdul Halin, Alfian Abdullah, Lili Nurliyana |
| author_sort | Ashour, Mohammed Waleed |
| collection | UPM |
| description | Being able to identify machining processes that produce specific machined surfaces is crucial in modern manufacturing production. Image processing and computer vision technologies have become indispensable tools for automated identification with benefits such as reduction in inspection time and avoidance of human errors due to inconsistency and fatigue. In this paper, the Support Vector Machine (SVM) classifier with various kernels is investigated for the categorization of machined surfaces into the six machining processes of Turning, Grinding, Horizontal Milling, Vertical Milling, Lapping, and Shaping. The effectiveness of the gray-level histogram as the discriminating feature is explored. Experimental results suggest that the SVM with the linear kernel provides superior performance for a dataset consisting of 72 workpiece images. |
| first_indexed | 2024-03-06T09:04:24Z |
| format | Conference or Workshop Item |
| id | upm.eprints-48220 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:04:24Z |
| publishDate | 2015 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-482202016-08-04T05:32:53Z http://psasir.upm.edu.my/id/eprint/48220/ Machining process classification using PCA reduced histogram features and the support vector machine Ashour, Mohammed Waleed Khalid, Fatimah Abdul Halin, Alfian Abdullah, Lili Nurliyana Being able to identify machining processes that produce specific machined surfaces is crucial in modern manufacturing production. Image processing and computer vision technologies have become indispensable tools for automated identification with benefits such as reduction in inspection time and avoidance of human errors due to inconsistency and fatigue. In this paper, the Support Vector Machine (SVM) classifier with various kernels is investigated for the categorization of machined surfaces into the six machining processes of Turning, Grinding, Horizontal Milling, Vertical Milling, Lapping, and Shaping. The effectiveness of the gray-level histogram as the discriminating feature is explored. Experimental results suggest that the SVM with the linear kernel provides superior performance for a dataset consisting of 72 workpiece images. IEEE 2015 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/48220/1/Machining%20process%20classification%20using%20PCA%20reduced%20histogram%20features%20and%20the%20support%20vector%20machine.pdf Ashour, Mohammed Waleed and Khalid, Fatimah and Abdul Halin, Alfian and Abdullah, Lili Nurliyana (2015) Machining process classification using PCA reduced histogram features and the support vector machine. In: 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA 2015), 19-21 Oct. 2015, Pullman Bangsar, Kuala Lumpur, Malaysia. (pp. 414-418). 10.1109/ICSIPA.2015.7412226 |
| spellingShingle | Ashour, Mohammed Waleed Khalid, Fatimah Abdul Halin, Alfian Abdullah, Lili Nurliyana Machining process classification using PCA reduced histogram features and the support vector machine |
| title | Machining process classification using PCA reduced histogram features and the support vector machine |
| title_full | Machining process classification using PCA reduced histogram features and the support vector machine |
| title_fullStr | Machining process classification using PCA reduced histogram features and the support vector machine |
| title_full_unstemmed | Machining process classification using PCA reduced histogram features and the support vector machine |
| title_short | Machining process classification using PCA reduced histogram features and the support vector machine |
| title_sort | machining process classification using pca reduced histogram features and the support vector machine |
| url | http://psasir.upm.edu.my/id/eprint/48220/1/Machining%20process%20classification%20using%20PCA%20reduced%20histogram%20features%20and%20the%20support%20vector%20machine.pdf |
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