A FRAMEWORK TO DEVELOP INTELLIGENT SYSTEM FOR CAPTURING PRODUCT FEATURES USING OPEN CV TECHNIQUE

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  • Yazid Abdullsameea Saif Faculty of Mechanical and Manufacturing Engineering, (UTHM) Malaysia
  • Yusri Yusof Faculty of Mechanical and Manufacturing Engineering, (UTHM) Malaysia
  • Maznah lliyas Ahmed Faculty of Mechanical and Manufacturing Engineering, (UTHM) Malaysia
  • Zohaib khan Pathan Faculty of Mechanical and Manufacturing Engineering, (UTHM) Malaysia
  • Kamran Latif Faculty of Mechanical and Manufacturing Engineering Technology, (UTeM), Malaysia
  • Aini Zuhra Abdul Kadir School of Mechanical Engineering, Faculty of Engineering, (UTM), Malaysia

Abstract

This research aims to propose an innovative framework using ISO14649 standard to detect defects in manufactured shaped objects or geometric surfaces of non-linear products of the CNC machine. The significant importance in order to recognize the potential to improve industry product quality inspection and encourage the waste of timing machines and product rejection. Open Computer Vision (Open CV) offers a smart, non-contact measurement and cost-effective technique to fulfil the requirements. The framework depends on the new technique of Open CV, which includes two parts: an intelligent selection of work-piece capturing the image for a particular inspection of the planar interfaces such as the hole, rectangular, pocket one, and the symmetric lighting model comparison approach for measurement of defects in the matched images. The contribution of this study is to build a structure in the computer vision method with a convolution neural network that predicts the classification of the feature for better accuracy and emphasizes the significant characteristics of the image processing technique coupled with experimental data on demanding image datasets and quality inspection measures.

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Published
2020-12-29
How to Cite
SAIF, Yazid Abdullsameea et al. A FRAMEWORK TO DEVELOP INTELLIGENT SYSTEM FOR CAPTURING PRODUCT FEATURES USING OPEN CV TECHNIQUE. Journal of Technology and Operations Management, [S.l.], v. 15, n. 2, p. 42-51, dec. 2020. ISSN 2590-4175. Available at: <http://e-journal.uum.edu.my/index.php/jtom/article/view/11465>. Date accessed: 16 apr. 2021. doi: https://doi.org/10.32890/jtom2020.15.2.11465.