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Friction Stir Welding Analysis Based on Equivalent Strain Method using Neural Networks
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  • Friction Stir Welding Analysis Based on Equivalent Strain Method using Neural Networks
  • Friction Stir Welding Analysis Based on Equivalent Strain Method using Neural Networks
저자명
Kang. Sung-Wook,Jang. Beom-Seon
간행물명
韓國海洋工學會誌
권/호정보
2014년|28권 5호|pp.452-465 (14 pages)
발행정보
한국해양공학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
서지반출

기타언어초록

The application of friction stir welding (FSW) technology has been extended to all industries, including shipbuilding. A heat transfer analysis evaluates the weldability of a welded work piece, and elasto-plastic analysis predicts the residual stress and deformation after welding. A thermal elasto-plastic analysis based on the heat transfer analysis results is most frequently used today. However, its application to large objects such as offshore structures and hulls is impractical owing to its long computational time. This paper proposes a new method, namely an equivalent strain method using the inherent strain, to overcome the disadvantages of the extended analysis time. In the present study, a residual stress analysis of FSW was performed using this equivalent strain method. Additionally, in order to reflect the external constraints in FSW, the reaction force was predicted using a neural network, Finally, the approach was verified by comparing the experimental results and thermal elasto-plastic analysis results for the calculated residual stress distribution.