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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 473495, 10 pages
http://dx.doi.org/10.1155/2013/473495
Research Article

Modeling and Analysis of the Weld Bead Geometry in Submerged Arc Welding by Using Adaptive Neurofuzzy Inference System

1Faculty of Technical Education, Sakarya University, Sakarya, Turkey
2Department of Mechatronics Engineering, Faculty of Technology, Sakarya University, Sakarya, Turkey
3Department of Mechanical Engineering, Faculty of Engineering, Sakarya University, Sakarya, Turkey
4Department of Business, Faculty of Business, Sakarya University, Sakarya, Turkey

Received 30 May 2013; Revised 29 August 2013; Accepted 13 September 2013

Academic Editor: Saeed Balochian

Copyright © 2013 Nuri Akkas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This study is aimed at obtaining a relationship between the values defining bead geometry and the welding parameters and also to select optimum welding parameters. For this reason, an experimental study has been realized. The welding parameters such as the arc current, arc voltage, and welding speed which have the most effect on bead geometry are considered, and the other parameters are held as constant. Four, three, and five different values for the arc current, the arc voltage, and welding speed are used, respectively. So, sixty samples made of St 52-3 material were prepared. The bead geometries of the samples are analyzed, and the thickness and penetration values of the weld bead are measured. Then, the relationship between the welding parameters is modeled by using artificial neural network (ANN) and neurofuzzy system approach. Each model is checked for its adequacy by using test data which are selected from experimental results. Then, the models developed are compared with regard to accuracy. Also, the appropriate welding parameters values can be easily selected when the models improve.