Earticle

초록

영어

Resistance welding is the most commonly implemented method to join steel sheets in the automobile industry. To increase the efficiency and to maintain or reduce the weight of vehicles, advanced high-strength steel (AHSS) has been developed as a build material for vehicular structures. This paper aims to exploit the ability of prediction for nugget sizes in resistance spot welding by using support vector regression (SVR) and artificial neural network (ANN) model. In this study, the nugget size will be predicted according to parameters in resistance spot welding, such as welding current, welding time and welding force, etc. using machine learning methods. In addition to considering important process parameters for resistance spot welding, some design parameters, such as the thickness of the materials and the coating, are also considered. As the experimental results, SVR shows better performance in the prediction of nugget size when compared to ANN.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Welability Prediction
  3.1. Resistance Spot Welding
  3.2. Support Vector Regression
  3.3. Artificial Neural Network
 4. Experimental Design
 5. Comparison Results of SVR and ANN
 6. Conclusion
 References

키워드

저자

• Huu Tan Tran [ Department of Electronics and computer Engineering, Chonnam National University ]
• Hyung Jeong Yang [ Department of Electronics and computer Engineering, Chonnam National University ]
• Kyoung-Yun Kim [ Department of Industricla and System Engineering, Wayne State University, Detroit, Mi, USA ]
• Raj Sohmshetty [ Ford Motor company, Dearborn, MI48124, USA ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605