Automatic Detection and Characterization of Weld Defects Using CNN Algorithm in Machine Learning

Authors

  • S. Ramesh Krishnan Department of Mechanical Engineering, Government Rajiv Gandhi Institute of Technology, Kerala, India
  • T. V. Abhishek Department of Mechanical Engineering, Government Rajiv Gandhi Institute of Technology, Kerala, India
  • Akhil Vinod Department of Mechanical Engineering, Government Rajiv Gandhi Institute of Technology, Kerala, India
  • Allen George Department of Mechanical Engineering, Government Rajiv Gandhi Institute of Technology, Kerala, India
  • C. Harikrishnan Department of Mechanical Engineering, Government Rajiv Gandhi Institute of Technology, Kerala, India

DOI:

https://doi.org/10.51983/arme-2021.10.1.2937

Keywords:

Radiography, CNN, Machine Learning, Digital Manufacturing

Abstract

Conventional radiographic technique uses visual inspection of scanned output for defect detection. This makes the inline testing of products time consuming and hectic. Convolutional Neural Network (CNN) algorithm in machine learning can be used for the automation of defect detection in radiography thereby reducing human intervention and associated delays. By the use of robotics the welding parameters can be adjusted and the issue of welding defects can be resolved. By combining the two, the defect detection process can be modified into a digital manufacturing process. A dataset created from radiography test data is used for training the algorithm and for writing a program to train this dataset which can be used for defect detection and its  characterization.

References

P. Elineudo Moura, R. Romeu Silva, H. S. Marcio Siqueira and João Marcos Rebello, “Pattern Recognition of Weld Defects in Preprocessed TOFD Signals Using Linear Classifiers,” Journal of Nondestructive Evaluation, Vol. 23, pp. 163-172, 2004.

Reza Nejatpour, Ali Asmari Sadabad, and Ali Akbar Akbari, “Automated weld defects detection using image processing and CAD methods,” ASME International Mechanical Engineering Congress and Exposition.Mechanical Systems and Control, Vol. 11, 2009.

N. Nacereddine, M. Zelmat, S. S. BelaYfa, and M.Tridi, “Weld defect detection in industrial radiography based digital image processing,” International Journal of Computer, Electrical, Automation, Control and Information Engineering, Vol. 1, pp. 433-436, 2007.

Saba Madani, and Mortaza Azizi, “Detection of Weld Defects in Radiography Films Using Image Processing,” Cumhuriyet Science Journal, Vol. 36, pp. 2397-2404, 2015.

H. Kasban, O. Zahran, H. Arafa, M. El-Kordy, S. M. S. Elaraby, and F. E. A. El-Samie, “Welding defect detection from radiographic image using cepstral approach,” Nondestructive Testing and Evaluation, Vol. 44, No. 2, pp. 226-231, 2011.

Ioannis Valavanis, and Dimitrios Kosmopoulos, “Defect detection and classification in weld radiographic images using geometric and texture features,” Expert Systems with Applications, Vol. 37, No. 12, pp. 7606-7614, 2010.

S. Sambath, P. Nagaraj, and N. Selvakumar, “Automatic Defect Classification in Ultrasonic NDT Using Artificial Intelligence,” Journal of Nondestructive Evaluation, Vol. 30, pp. 20-28, 2011.

Juan Zapata, Rafael Vilar, and Ramón Ruiz, “Automatic Inspection System of WeldingRadiographic Images Based on ANN Under a Regularisation Process,” Journal of Nondestructive Evaluation, Vol. 31, pp. 34-45, 2012.

M. Tom Mitchell, Machine Learning, McGraw-Hill Science/ Engineering/Math, 1997.

[Online].Available: https://www.geeksforgeeks.org/machine-learning

M. V. Valueva, N. N. Nagornov, P. A. Lyakhov, G. V. Valuev and N. I. Chervyakov, “Application of the residue number system to reduce hardware costs of the convolutional neural network implementation,” Mathematics and Computers in Simulation, Vol. 177, pp. 232-243, 2020.

H. Samuel Huang, Peng Liu, Abhiram Mokasdar, and Liang Hou, “Additive manufacturing and its societal impact: a literature review,” The International Journal of Advanced Manufacturing Technology, Vol. 67, pp. 1191-1203, 2013.

Ciresan Dan, Ueli Meier, Jonathan Masci, M. Luca Gambardella, and Jurgen Schmidhuber, “Flexible, High Performance Convolutional Neural Networks for Image Classification”, Artificial Intelligence, Vol. 2, pp. 1237-1242, 2011.

Roman Sizyakin, Viacheslav Voronin, Nikolay Gapon, Aleksandr Zelensky, and Aleksandra Pižurica, Automatic detection of welding defects using the convolutional neural network, Automated Visual Inspection and Machine Vision III, 11061, 2019.

S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” International Conference on Engineering and Technology, pp. 1-6, 2017.

Adrian Kaehler, and Gary Bradski, “Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library,” Computer Programming/Robotics, O' Reilly Media, 2016.

Martin Abadi et al., “Tensor Flow: Large-scale machine learning on heterogeneous distributed systems,” [Online]. Available: https://www.tensorflow.org, 2015.

[Online]. Available: https://www.tensorflow.org/guide/effective_tf2, 2019.

[Online]. Available: https://scikitlearn.org/0.20/_downloads/scikit-learndocs.pdf.https://scikit-learn.org, 2019.

Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Perrot, and Édouard Duchesnay, “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, Vol. 12, pp. 2825-2830, 2011.

Cortes Corinna, Vapnik, and N. Vladimir, “Support-vector networks,” Machine Learning, Vol. 20, No. 3, pp. 273-297, 1995.

[Online]. Available: https://blog.keras.io/keras-as-a-simplified-interface-to-tensorflow-tutorial.html, 2016.

Chris Basoglu, CNTK_2_7_Release_Notes-Cognitive Tool kit, 2020.

Yuqing Tang, and Sayan Pathak, “Scalable Deep Learning with Microsoft Cognitive Toolkit,” [Online]. Available: https://cntk.ai/Tutorials/AAAI18/CNTK%20Tutorial_AAAI_Feb_2018_final.pdf, 2018.

[Online]. Available: https://www.geeksforgeeks.org/theano-in-python.

Luis Lanca, and Augus to Silva, “Digital Radiography Detectors: A Technical Overview,” Digital imaging systems for plain radiography, Springer, pp. 14-17, 2013.

Statistical Parametric Mapping. [Online]. Available: https://www.fil.ion.ucl.ac.uk.

V. John Guttag, Introduction to Computation and Programming Using Python, MIT Press, ISBN978-0-262-52962-4, 2016.

M. Scott Shell, An introduction to Python for scientific computing, Free Tech Books, 2019.

Jianzhuang Liu, Wenqing Li, and Yupeng Tian, “Automatic thresholding of gray-level pictures using two-dimension Otsu method”, International Conference on Circuits and Systems, pp. 325-327, 1991.

Zhang, Jun Hu, and Jinglu, “Image segmentation based on 2D Otsu method with histogram analysis,” International Conference on Computer Science and Software Engineering, Vol. 6, pp. 105-108, 2008.

Huang, and Deng-Yuan, “Optimal multi-level thresholding using a two-stage Otsu optimization approach,” Pattern Recognition Letters, Vol. 30, No. 3, pp. 275-284, 2009.

Geoffrey Hinton, “Some demonstrations of the effects of structural descriptions in mental imagery,” Cognitive Science, Vol. 3, No. 3, pp. 231-250, 1979.

Downloads

Published

15-05-2021

How to Cite

Ramesh Krishnan, S., T. V. Abhishek, Vinod, A. ., George, A. ., & C. Harikrishnan. (2021). Automatic Detection and Characterization of Weld Defects Using CNN Algorithm in Machine Learning. Asian Review of Mechanical Engineering, 10(1), 26–35. https://doi.org/10.51983/arme-2021.10.1.2937

Issue

Vol. 10 No. 1 (2021): January-June 2021

Section

Articles

License

Copyright (c) 2021 The Research Publication

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.