AN EFFICIENT QUALITY CONTROL SYSTEM BY MACHINE LEARNING FOR SURFACE DEFECTS
DOI:
https://doi.org/10.29284/ijasis.7.2.2021.40-48Keywords:
Gabor expansion, principal component analysis, local thresholding, defect detection system, raw alloy surface.Abstract
Quality control plays a crucial role to meet the high and accurate quality of production in many manufacturing industries. The high quality products may become unreliable due to surface defects. Generally, quality control is more important in automotive industry such as in the field of car-body parts manufacturing. The exterior appearance of the car body should be smooth surfaces and edges with flawless nature. In order to build such flawless body parts, surface defect detection system is taken into account. In this paper, an Automated Defect Detection (ADD) system is presented. The design of the ADD system consists of two steps. The first step is considered as a classification system where the given image is classified into defected or non-defected using Gabor expansion with Principal Component Analysis (PCA). The next step is segmentation where the region of defect is identified using local thresholding. The evaluation is performed on raw alloy steel surface and machined surfaces of steel and cast iron. Results prove that the ADD system classify the input image into defect/no defect with 100% accuracy by a simple nearest neighbor classifier and with 94.5% detection accuracy for the segmentation system.
Downloads
References
I. I. Artemov, V. D. Krevchik, S. V. Kochkin, A. V. Sokolov, N. P. Simonov and N. E. Artemova, "Measurement and Quality Control of a Nanomodified Surface Layer of Machine Parts," International Conference Quality Management, Transport and Information Security, Information Technologies, 2020, pp. 184-187.
L. Xiaodong, M. Weijie and J. Wei, "Image recognition for steel ball's surface quality detecting based on kernel extreme learning machine," 34th Chinese Control Conference, 2015, pp. 3727-3731.
Li Jian, Han Wei and He Bin, "Research on inspection and classification of leather surface defects based on neural network and decision tree," International Conference On Computer Design and Applications, 2010, pp. V2-381-V2-384.
L. Zhou, H. Liu, X. Zhuang and D. Liu, "Study on Brittle Graphite Surface Roughness Detection Based on Gray-Level Co-occurrence Matrix," 3rd International Conference on Mechanical, Control and Computer Engineering, 2018, pp. 273-276.
M. Sadeghi, S. Sadat valadie some saraie and A. Mahdeian, "Application of two dimensional wavelet for defect detection in steel process," 2nd International Conference on Control, Instrumentation and Automation, 2011, pp. 1160-1163
Z. F. Hocenski and E. K. Nyarko, "Surface quality control of ceramic tiles using neural networks approach," Proceedings of the 2002 IEEE International Symposium on Industrial Electronics, 2002, pp. 657-660.
D. Amin and S. Akhter, "Deep Learning-Based Defect Detection System in Steel Sheet Surfaces," IEEE Region 10 Symposium, 2020, pp. 444-448.
S. Niu, B. Li, X. Wang and H. Lin, "Defect Image Sample Generation With GAN for Improving Defect Recognition," IEEE Transactions on Automation Science and Engineering, Vol. 17, No. 3, 2020, pp. 1611-1622.
G. K. Nand, Noopur and N. Neogi, "Defect detection of steel surface using entropy segmentation," Annual IEEE India Conference, 2014, pp. 1-6.
Y. Gao and Y. Yang, "Classification based on multi-classifier of SVM fusion for steel strip surface defects," Proceedings of the 32nd Chinese Control Conference, 2013, pp. 3617-3622.
J. Ma, Y. Wang, C. Shi and C. Lu, "Fast Surface Defect Detection Using Improved Gabor Filters," 25th IEEE International Conference on Image Processing, 2018, pp. 1508-1512.
Juan-juan Gu, Jun Zhang and Liang Tao, "DCT kernel based finite discrete Gabor expansion and transform implemented by filterbanks," International Conference on Computer Application and System Modeling, 2010, pp. V1-569-V1-573.
L. Tao, H. K. Kwan and J. Gu, "Filterbank-based fast parallel algorithms for real-valued discrete gabor expansion and transform," Proceedings of IEEE International Symposium on Circuits and Systems, 2010, pp. 2674-2677.
A. Rehman, A. Khan, M. A. Ali, M. U. Khan, S. U. Khan and L. Ali, "Performance Analysis of PCA, Sparse PCA, Kernel PCA and Incremental PCA Algorithms for Heart Failure Prediction," International Conference on Electrical, Communication, and Computer Engineering, 2020, pp. 1-5.
Hongchuan Yu and M. Bennamoun, "1D-PCA, 2D-PCA to nD-PCA," 18th International Conference on Pattern Recognition, 2006, pp. 181-184.
R. Ghoshal and A. Banerjee, "An improved scene text and document image binarization scheme," 4th International Conference on Recent Advances in Information Technology, 2018, pp. 1-6
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
