Edge detection of Friction Stir Welded Joints by using Fourier Transformation

International Journal of Machine Learning and Networked Collaborative Engineering

View Publication Info
 
 
Field Value
 
Title Edge detection of Friction Stir Welded Joints by using Fourier Transformation
 
Creator Mishra, Akshansh
Sarawagi, Rajat
Pathak, Tarushi
 
Subject Machine Learning
Friction Stir Welding
Computer Vision
Fourier Transformation
 
Description Friction Stir Welding process often results good quality weld joints in comparison to the weld joint fabricated by the conventional welding process. But there are chances of formation of various defects if the input parameters are not selected properly. In our case study, we have constructed an image based defect recognition system by using Fourier transformation method. Five types of filters i.e. Ideal Filter, Butterworth Filter, Low pass Filter, Gaussian Filter and High Pass Filter were used. The results showed that the high pass filter has more capability to detect the edges in comparison to other four filters. It was also observed that Ideal filter has a lot of distortions when compared to the Gaussian Filter and Butterworth Filter.
 
Publisher SR Informatics, New Delhi, India
 
Date 2020-10-24
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://www.mlnce.net/index.php/Home/article/view/113
 
Source International Journal of Machine Learning and Networked Collaborative Engineering; Vol. 4 No. 2 (2020): Volume No 04 Issue No 02 (2020); 53-62
2581-3242
 
Language eng
 
Relation http://www.mlnce.net/index.php/Home/article/view/113/83
 
Rights Copyright (c) 2020 International Journal of Machine Learning and Networked Collaborative Engineering
http://creativecommons.org/licenses/by-nc-nd/4.0
 

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