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<p>In this work, the parameter identification method was proposed for the double-ellipsoidal heat source model in metal inert gas welding based on the inverse analysis using welding-induced residual stress. Two hollow S45C steel specimens were prepared for welding. The surface and internal residual stress was measured using X-Ray Diffraction (XRD). For the parameter identification of the heat source model, inverse analysis was conducted through an evolutionary optimization algorithm in ABAQUS/ISIGHT where two numerical simulations of welding and residual stress prediction were successively performed with the given parameters of heat source model by the optimization algorithm. The objective function in the inverse analysis was defined as the root-mean-square error between the measured and predicted residual stress on the surface and internal of material. The results showed that the proposed method could effectively identify the parameters of the heat source model in welding with high accuracy in predicting welding-induced residual stress.</p>

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Residual Stress-Based Inverse Analysis for Parameter Identification of a Double-Ellipsoidal Heat Source Model in Metal Inert Gas Welding

  • 期刊论文

  • 发表日期：

    2025年7月20日

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  • Hyunsung Choi,
  • Dong Jun Lee,
  • Yunji Cho,
  • Min Jae Beak,
  • Hyeonil Park,
  • Moo-Young Seok,
  • Yong Nam Kwon,
  • Hyun-Min Sung,
  • Dooyoung Kim

摘要

In this work, the parameter identification method was proposed for the double-ellipsoidal heat source model in metal inert gas welding based on the inverse analysis using welding-induced residual stress. Two hollow S45C steel specimens were prepared for welding. The surface and internal residual stress was measured using X-Ray Diffraction (XRD). For the parameter identification of the heat source model, inverse analysis was conducted through an evolutionary optimization algorithm in ABAQUS/ISIGHT where two numerical simulations of welding and residual stress prediction were successively performed with the given parameters of heat source model by the optimization algorithm. The objective function in the inverse analysis was defined as the root-mean-square error between the measured and predicted residual stress on the surface and internal of material. The results showed that the proposed method could effectively identify the parameters of the heat source model in welding with high accuracy in predicting welding-induced residual stress.