Computer Vision Algorithm for Surface Defects Identification in TIG Welded Joints

Open Access

Year : 2023 | Volume : | : | Page : –
By

Sunil Kumar,

Adarsh Kumar,

  1. Research Scholar Department of Mechanical Engineering, Bansal Institute of Engineering and Technology India
  2. Assistant Professor Department of Mechanical Engineering, Bansal Institute Of Engineering and Technology India

Abstract

Quality monitoring of welded joints is still quite a tedious job for many industries. The surface defects on welded joints arises from the improper selection of various input parameters which further result bad quality weld. This paper highlight the application of a computer vision algorithm called discrete Fourier transformation approach for identification of surface defects present on TIG welded joints.

Keywords: Machine vision, surface defects, TIG welding, Fourier transformation

How to cite this article: Sunil Kumar, Adarsh Kumar. Computer Vision Algorithm for Surface Defects Identification in TIG Welded Joints. Research & Reviews : Journal of Computational Biology. 2023; ():-.
How to cite this URL: Sunil Kumar, Adarsh Kumar. Computer Vision Algorithm for Surface Defects Identification in TIG Welded Joints. Research & Reviews : Journal of Computational Biology. 2023; ():-. Available from: https://journals.stmjournals.com/rrjocb/article=2023/view=90073

Full Text PDF Download


References

1. Jain R, Kasturi R, Schunck BG. Machine vision. Vol. 5. New York: McGraw-Hill; 1995.
2. Davies ER. Machine vision: theory, algorithms, practicalities. Elsevier; 2004.
3. Chen YR, Chao K, Kim MS. Machine vision technology for agricultural applications. Comput Electron Agric. 2002;36(2–3):173–91. doi: 10.1016/S0168–1699(02)00100-X.
4. Oren M, Nayar SK. Generalization of the Lambertian model and implications for machine vision. Int J Comput Vision. 1995;14(3):227–51. doi: 10.1007/BF01679684.
5. Pérez L, Rodríguez Í, Rodríguez N, Usamentiaga R, García DF. Robot guidance using machine vision techniques in industrial environments: A comparative review. Sensors (Basel). 2016;16(3):335. doi: 10.3390/s16030335, PMID 26959030.
6. Vernon D. Machine vision-

Open Access Article
Volume
Received March 9, 2021
Accepted April 20, 2021
Published January 7, 2023
Views: 1,456