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oai:gredos.usal.es:10366/1460922025-06-05T12:36:20Zcom_10366_145966com_10366_122682com_10366_4666com_10366_3823col_10366_145968

00925njm 22002777a 4500 dc Mishra, Akshansh author 2020-06-19 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. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 9 (2020) 2255-2863 http://hdl.handle.net/10366/146092 Machine Learning Friction Stir Welding Local Binary Pattern Machine Vision Local binary pattern for the evaluation of surface quality of dissimilar Friction Stir Welded Ultrafine Grained 1050 and 6061-T6 Aluminium Alloys