Local binary pattern for the evaluation of surface quality of dissimilar Friction Stir Welded Ultrafine Grained 1050 and 6061-T6 Aluminium Alloys

• Published in: Advances in distributed computing and artificial intelligence journal Vol. 9; no. 2; pp. 69 - 77
• Main Author: Mishra, Akshansh
• Format: Journal Article
• Language: English
• Published: Salamanca Ediciones Universidad de Salamanca 01.01.2020
• Subjects: Aerospace industry; Algorithms; Aluminum base alloys; Axial forces; Edge joints; Feature extraction; Friction stir welding; Grooves; local binary pattern; Machine learning; machine vision; Surface defects; Surface properties; Texture; Ultrafines; Weight reduction; Welded joints; Welding parameters
• ISSN: 2255-2863; 2255-2863
• DOI: 10.14201/ADCAIJ2020926977

Friction Stir Welding process is an advanced solid-state joining process which finds application in various industries like automobiles, manufacturing, aerospace and railway firms. Input parameters like tool rotational speed, welding speed, axial force and tilt angle govern the quality of Friction Stir Welded joint. Improper selection of these parameters further leads to fabrication of the joint of bad quality resulting groove edges, flash formation and various other surface defects. In the present work, a texture based analytic machine learning algorithm known as Local Binary Pattern (LBP) is used for the extraction of texture features of the Friction Stir Welded joints which are welded at a different rotational speed. It was observed that LBP algorithm can accurately detect any irregularities present on the surface of Friction Stir Welded joint.