Machining process classification using PCA reduced histogram features and the support vector machine

Citation

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).

Abstract

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.

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Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Faculty of Computer Science and Information Technology
DOI Number:	https://doi.org/10.1109/ICSIPA.2015.7412226
Publisher:	IEEE
Keywords:	Artificial nueral networks; Gray-level histogram; Machined surface; Principal component analysis; Support vector machine; Texture classification
Depositing User:	Nabilah Mustapa
Date Deposited:	04 Aug 2016 05:32
Last Modified:	04 Aug 2016 05:32
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ICSIPA.2015.7412226
URI:	http://psasir.upm.edu.my/id/eprint/48220
Statistic Details:	View Download Statistic

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