Local binary pattern defect recognition approach for the friction stir welded AA 1200 and AA 6061-T6 aluminum alloy

DOI: https://doi.org/10.30464/jmee.2020.4.1.27
Keywords: Local binary patterns, Friction stir welding, Machine learning, Surface defects

Abstract

The research reported in this paper focuses on the application of Local Binary Patterns (LBPs) for surface defects detection. The surface defection detection algorithm for Friction Stir Welded aluminum plates is the key part of the entire surface defect recognition system. Two different grades i.e AA 1200 and AA 6061 plates were similarly joined with the help of Friction Stir Welding process. Python codes for the proposed algorithm were executed on Google Colaboratory platform. The results obtained prove that the Local Binary Patterns method can be used for real-time surface defects detection in Friction Stir Welded joints.

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Author Biography

Akshansh Mishra, Stir Research Technologies, Uttar Pradesh, India

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Akshansh Mishra is a founder of Stir Research Technologies which deals in collaborative research in Artificial Intelligence and Friction Stir Welding. Currently, he now works as a Principal Deep Learning Scientist in Codes & Coffee.  He had developed the first MOOC on Friction Stir Welding which is available on Udemy. His main research interests are Friction Stir Welding, Artificial Neural Network and Reinforcement Learning. He has published 7 research books dealing with Friction Stir Welding, Composites, Laser Welding and Artificial Intelligence which are available on Amazon.

JMME 4(1) 2020 front cover
Published
2020-08-09
How to Cite
Mishra, A. (2020) “Local binary pattern defect recognition approach for the friction stir welded AA 1200 and AA 6061-T6 aluminum alloy”, Journal of Mechanical and Energy Engineering, 4(1), pp. 27-32. doi: 10.30464/jmee.2020.4.1.27.
Issue
Vol 4 No 1 (2020)
Section
Mechanical Engineering

Copyright (c) 2020 Akshansh Mishra

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