Machine Learning Approach for Defects Identification in Dissimilar Friction Stir Welded Aluminium Alloys AA 7075-AA 1100 Joints
- 1 Politecnico Di Milano, Italy
Machine learning approaches are now applied in various manufacturing industries. Various machine learning algorithms can be implemented for prediction of the particular mechanical properties like Ultimate Tensile Strength (UTS), Elongation percentage and fracture strength of the given mechanical component and also image processing algorithms can be applied for defects detection in the mechanical components. In our recent work, we have used a novel machine learning approach for the detection of the surface defects in dissimilar Friction Stir Welded joints by using Local Binary Pattern (LBP) algorithm. The results obtained are satisfying and it is concluded that the LBP can be implemented in the detection of surface defects.
Copyright: © 2020 Akshansh Mishra. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- Friction Stir Welding
- Local Binary Pattern
- Machine Learning
- Surface Defects