Friction Stir Welding Analysis Based on Equivalent Strain Method using Neural Networks
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초록
The application of friction stir welding (FSW) technology has been extended to all industries, including shipbuilding. A heat transferanalysis evaluates the weldability of a welded work piece, and elasto-plastic analysis predicts the residual stress and deformation after welding. Athermal elasto-plastic analysis based on the heat transfer analysis results is most frequently used today. However, its application to large objectssuch as offshore structures and hulls is impractical owing to its long computational time. This paper proposes a new method, namely an equivalentstrain method using the inherent strain, to overcome the disadvantages of the extended analysis time. In the present study, a residual stressanalysis of FSW was performed using this equivalent strain method. Additionally, in order to reflect the external constraints in FSW, the reactionforce was predicted using a neural network, Finally, the approach was verified by comparing the experimental results and thermal elasto-plasticanalysis results for the calculated residual stress distribution.
Abstract
The application of friction stir welding (FSW) technology has been extended to all industries, including shipbuilding. A heat transferanalysis evaluates the weldability of a welded work piece, and elasto-plastic analysis predicts the residual stress and deformation after welding. Athermal elasto-plastic analysis based on the heat transfer analysis results is most frequently used today. However, its application to large objectssuch as offshore structures and hulls is impractical owing to its long computational time. This paper proposes a new method, namely an equivalentstrain method using the inherent strain, to overcome the disadvantages of the extended analysis time. In the present study, a residual stressanalysis of FSW was performed using this equivalent strain method. Additionally, in order to reflect the external constraints in FSW, the reactionforce was predicted using a neural network, Finally, the approach was verified by comparing the experimental results and thermal elasto-plasticanalysis results for the calculated residual stress distribution.
참고문헌 (26)
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