Backside Weld Bead Shape Modeling Using Support Vector Machine

Hang Dong, Shelby A. Huff, Ming Cong, Yuming Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Modeling characteristic performance of the arc welding process is an underlying task for online weld quality control. As a continuous yet complex high-thermal welding process, conventional modelling method can be ineffective in the presence of big uncertainties and noise. Besides, experiments and evaluations could be costly and very inefficient because of resource utilization, energy consumption, and dedicated human labor. Hence, we investigate the welding process modelling problem in this paper to understand how to tune the welding parameters to achieve the desired characteristic performance. Support Vector Machine, a non-parametric modeling technique, is exploited to solve model problem. Cross-validation method is employed to avoid overfitting problem. Gas tungsten arc welding experiments were performed and the experimental data were collected and utilized to validate the proposed modelling method. The predicted characteristic performance is compared with the original data and it shows that the proposed method can accurately predict the weld bead geometry. This paper opens a door for online parameter tuning to achieve optimal performance, hence the proposed method will innovate the welding process.

Original languageEnglish
Title of host publication2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
Pages277-282
Number of pages6
DOIs
StatePublished - Aug 24 2018
Event7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017 - Honolulu, United States
Duration: Jul 31 2017Aug 4 2017

Publication series

Name2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017

Conference

Conference7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
Country/TerritoryUnited States
CityHonolulu
Period7/31/178/4/17

Bibliographical note

Funding Information:
M. Cong∗ (corresponding author) and H. Dong are with the Department of Mechanical Engineering, Dalian University of Technology, Email: congm@dlut.edu.cn, Tel. (+86) 13130491926 Shelby.A Huff is with the Ingram School of Engineering, Texas State University, USA. Y. Zhang is with the Department of Electrical and Computer Engineering, University of Kentucky This work is supported by Key Scientific and Technological Project of Liaoning Province (No. 2015080009-201) to M. Cong.

Funding Information:
ACKNOWLEDGMENT The first author would like to thank Chinese Scholarship Council to provide the financial support for his one year (2015-2016) exchange Ph.D. studentship at Taxes State University.

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization

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