CLASSIFICATION OF WELD BEAD DEFECTS BASED ON IMAGE SEGMENTATION METHOD
Abstract
Defect is an imperfection that could impair the worth and utility of a finished good. The defects show some disorder of the product and it is opposite the standard or criteria that have been stated. In defining and detecting the defects occur, many ways have been discussed and observed. However, the techniques or ways are not appropriate or not suitable for some condition and situation. In addition, welding process is one of the critical processes in detecting and defining defect to ensure the quality of the weld bead. To overcome the problem in detecting and defining defects, image processing is one of the methods in improving the process of detecting defects. The defects are classified based on automatic thresholding method that automates detecting and defining the defects. This study proposes a Decision Tree-based classification of weld bead defects through segmentation of image. The result obtained shows that the classification is effective in identifying the weld bead defects with 89% accuracy. For future work, the focus will be made to improve the detection accuracy by integrating suitable filters.
Downloads
References
J. Ahaiwe, “Digital Image Processing: An Overview of Computational Time Requirement,” International Journal of Engineering Sciences and Research Technology, vol. 2, no. 8, pp. 2148-2152, 2013.
Z. Al-Ameen, G. Sulong, A. Rehman, M. Al-Rodhaan, T. Saba and A. Al-Dhelaan, “Phase-preserving approach in denoising computed tomography medical images,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 5, no. 1, pp. 16-26, 2014.
M.M.P. Dan, A.Y.B. Hashim, R.A.B Adnan, Z. Ruzaidi, A.A.R. Khairul, M.S. Rizal and S.P. Anton, “Computer Vision Based Robotic Polishing Using Artificial Neural Networks,” Journal of Advanced Manufacturing Technology, vol. 6, no. 1, pp. 61-76, 2012.
N. Awang, M.H.F.M. Fauadi and N. S. Rosli, “Image Processing of Product Surface Defect Using Scilab,” Applied Mechanics and Materials, vol. 789-790, pp. 1223-1226, 2015.
N.S. Rosli, M.H.F.M. Fauadi, N.F. Awang and A.Z.M. Noor, “Vision-Based Defects Detection for Glass Production based on Improved Image Processing Method,” Journal of Advanced Manufacturing Technology, vol. 12, no. 1(1), pp. 203-212, 2018.
J. Galbally, S. Marcel and J. Fierrez, “Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition,” IEEE Transactions on Image Processing, vol. 23, no. 2, pp. 710-724, 2014.
M.J. Er, S. Wu, J. Lu and H.L. Toh, “Face recognition with radial basis function (RBF) neural networks,” IEEE Transactions on Neural Networks, vol. 13, no. 3, pp. 697-710, 2002.
N. Zehngut, F. Juefei-Xu, R. Bardia, D. K. Pal, C. Bhagavatula and M. Savvides, “Investigating the feasibility of image-based nose biometrics,” in IEEE International Conference on Image Processing (ICIP), Quebec City, 2015, pp. 522-526.
A.K. Singh, D. Tapas, D. Vidyut and R.N. Rai, “An approach to maximize weld penetration during TIG welding of P91 steel plates by utilizing image processing and Taguchi orthogonal array,” Journal of The Institution of Engineers (India): Series C, vol. 98, no. 5, pp. 541-551, 2017.
L.S. Rosado, T.G. Santos, M. Piedade, P.M. Ramos and P. Vilaça, “Advanced technique for non-destructive testing of friction stir welding of metals,” Measurement, vol. 43, no. 8, pp. 1021-1030, 2010.
S. Kalpakjian and S. Schmid, Manufacturing, Engineering and Technology, 5th Edition. Upper-Saddle River, NJ: Pearson, 2006.
K. Iyshwerya, B. Janani, S. Krithika and T. Manikandan, “Defect detection algorithm for high speed inspection in machine vision,” in IEEE International Conference on Smart Structures and Systems (ICSSS), Chennai, 2013, pp. 103-107.
G. Senthil Kumar, U. Natarajan and S.S. Ananthan, “Vision inspection system for the identification and classification of defects in MIG welding joints,” The International Journal of Advanced Manufacturing Technology, vol. 61, no. 9–12, pp 923–933, 2012.
Y. Li, Y.F. Li, Q.L. Wang, D. Xu and M. Tan, “Measurement and Defect Detection of the Weld Bead Based on Online Vision Inspection,” IEEE Transactions on Instrumentation and Measurement, vol. 59, no. 7, pp. 1841-1849, 2010.
S.M. Kim, Y.C. Lee and S.C. Lee, “Vision Based Automatic Inspection System for Nuts Welded on the Support Hinge,” in SICE-ICASE International Joint Conference, Busan, 2006, pp. 1508-1512.
H.C. Nguyen and B.R. Lee, “Laser-vision-based quality inspection system for small-bead laser welding,” International Journal of Precision Engineering and Manufacturing, vol. 15, no. 3, pp. 415–423, 2014.
H. Chen, J. Li, X. Zhang and Z. Deng, “Application of visual servoing to an X-ray based welding inspection robot,” in International Conference on Control and Automation, Budapest, 2005, pp. 977-982.
R. Stojanovic, P. Mitropulos, C. Koulamas, Y. Karayiannis, S. Koubias and G. Papadopoulos, “Real-Time Vision-Based System for Textile Fabric Inspection,” Real-Time Imaging, vol. 7, no. 6, pp. 507-518, 2001.
M. Rafael, M. Delgado, T. Mezzadri, R. D. da Silva, M. Vaz and C. Marinho, “WBdetect: Particle Swarm Optimization for Segmenting Weld Beads in Radiographic Images,” Designing with Computational Intelligence, vol. 664, pp. 217-236, 2017.
Z. Jun and J. Hu, “Image segmentation based on 2D Otsu method with histogram analysis,” in IEEE 2008 International Conference on Computer Science and Software Engineering, Hubei, 2008, pp. 105-108.
