
Welding Defect Identification with Machine Vision System using Machine Learning
Author(s) -
R Praveen Kumar,
R. Deivanathan,
R. Jegadeeshwaran
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1716/1/012023
Subject(s) - welding , machine vision , artificial intelligence , join (topology) , computer science , process (computing) , support vector machine , identification (biology) , matlab , friction welding , machine learning , computer vision , pattern recognition (psychology) , mechanical engineering , engineering , mathematics , operating system , botany , combinatorics , biology
Friction stir welding is a solid-state joining process to join similar or dissimilar metals, which uses the friction developed between the metals to join them. Friction stir welding is an efficient way to join the metals but the welding defects are a little difficult to find through naked eyes. Hence, there is a chance of it being unnoticed even in final inspection in industries. So the welded joints are inspected by machine vision, using camera and an intelligent system. After obtaining the images of the welded joints and processing them using MATLAB, they are differentiated according to the various defects in them, using machine learning technique. For this, the statistical features of the image are extracted and they are classified into different defects using classifiers like Decision Trees, Discriminant Analysis, Support Vector Machine and Nearest Neighbour.