Novel Approach for Surface Defect Analysis of Friction Stir Welded Lightweight Automotive Metal Alloys by using Local Binary Patterns

Authors

  • Akshansh Mishra Centre for Artificial Intelligence and Friction Stir Welding, Stir Research Technologies, Uttar Pradesh, India. https://orcid.org/0000-0003-4939-359X
  • Vikeshachand Prasad Singh Department of Thermo-fluid Engineering, North Eastern Regional Institute of Science and Technology, Nirjuli, Arunachal Pradesh, India.
  • Shubham Maurya Department of Mechanical Engineering, Babu Banarasi Das National Institute of Technology and Management, Lucknow, Uttar Pradesh, India.

Abstract

Friction Stir Welding is a revolutionary solid-state joining process that overcomes the limitations of the conventional welding process. However, none of the welded joints are free from surface defects which can be due to improper selection of welding input parameters such as tool rotational speed, tool traverse speed, axial force, etc. In the present work, a novel approach is used for surface defects detection from the six samples of Friction Stir Welded joints of Aluminium 6060-T5 plates with the help of image processing technique known as Local Binary Patterns (LBP). The LBP images of the samples obtained showed very promising results that can be implemented for the detection of surface defects.

How to cite this article: Mishra A, Singh VP, Maurya S. Novel Approach for Surface Defect Analysis of Friction Stir Welded Lightweight Automotive Metal Alloys by using Local Binary Patterns. J Adv Res Mfg Mater Sci Met Engi 2020; 7(1&2): 8-14.

DOI: https://doi.org/10.24321/2393.8315.202001

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Published

2020-06-26