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.

R. Nejatpour, A. A. Sadabad, and A. A. Akbari, "Automated weld defects detection using image processing and CAD methods," in 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.

S. Madani and M. Azizi, "Detection of Weld Defects in Radiography Films Using Image Processing," Cumhuriyet Science Journal, vol. 36, pp. 2397-2404, 2015.

H. Kasban et al., "Welding defect detection from radiographic image using cepstral approach," Nondestructive Testing and Evaluation, vol. 44, no. 2, pp. 226-231, 2011.

I. Valavanis and D. 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.

J. Zapata, R. Vilar, and R. Ruiz, "Automatic Inspection System of Welding Radiographic Images Based on ANN Under a Regularisation Process," Journal of Nondestructive Evaluation, vol. 31, pp. 34-45, 2012.

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

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

M. V. Valueva et al., "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. S. Huang et al., "Additive manufacturing and its societal impact: a literature review," The International Journal of Advanced Manufacturing Technology, vol. 67, pp. 1191-1203, 2013.

C. Dan et al., "Flexible, High Performance Convolutional Neural Networks for Image Classification," Artificial Intelligence, vol. 2, pp. 1237-1242, 2011.

R. Sizyakin et al., "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," in International Conference on Engineering and Technology, pp. 1-6, 2017.

A. Kaehler and G. Bradski, Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library. O' Reilly Media, 2016.

M. 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, 2019.

F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.

C. Cortes and V. Vapnik, "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.

C. Basoglu, CNTK_2_7_Release_Notes-Cognitive Tool kit, 2020.

Y. Tang and S. 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.

L. Lanca and A. Silva, "Digital Radiography Detectors: A Technical Overview," in Digital imaging systems for plain radiography, Springer, pp. 14-17, 2013.

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

V. J. Guttag, Introduction to Computation and Programming Using Python. MIT Press, 2016.

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

J. Liu, W. Li, and Y. Tian, "Automatic thresholding of gray-level pictures using two-dimension Otsu method," in International Conference on Circuits and Systems, pp. 325-327, 1991.

J. Zhang, J. Hu, and L. Jinglu, "Image segmentation based on 2D Otsu method with histogram analysis," in International Conference on Computer Science and Software Engineering, vol. 6, pp. 105-108, 2008.

Y. Huang and D. Yuan, "Optimal multi-level thresholding using a two-stage Otsu optimization approach," Pattern Recognition Letters, vol. 30, no. 3, pp. 275-284, 2009.

G. E. Hinton, "Some demonstrations of the effects of structural descriptions in mental imagery," Cognitive Science, vol. 3, no. 3, pp. 231-250, 1979.

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Published

23-04-2021

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