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<p>The formation quality of welded joints is influenced by the dynamic behavior of the molten pool, but existing visual sensing technologies are unable to directly measure its internal physical properties. Although numerical simulations play an important role in characterizing the welding formation process, their low computational efficiency and long processing time create a bottleneck in application. To address this, the study proposes an approach that integrates digital twins with numerical simulations to achieve precise and efficient prediction of this process. Firstly, a high-fidelity cold metal transfer welding digital twin model was developed on a finite element simulation platform, encompassing the geometry, physics, behavior, and rules of the welding formation process, and its reliability was verified through simulations of stainless steel welding joints. Subsequently, an improved conditional deep convolutional generative adversarial network (C-DCGAN) was designed as a lightweight surrogate model, incorporating affine mapping and residual structures, which efficiently replaced traditional numerical simulations while ensuring high accuracy and stability. The results show that the improved C-DCGAN model achieves higher predictive accuracy and efficiency than the comparative models at all three welding speeds. At a welding speed of 10&#xa0;mm/s, the mean value of the MSE for molten pool image generation is as low as 0.0059, with good robustness. Meanwhile, the prediction time of the model requires only 5.44&#xa0;s, significantly reducing the computation time compared to traditional numerical simulations and the comparative models. This study provides a foundation for the implementation of digital twin systems and advanced manufacturing quality control in industrial settings.</p>

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A lightweight digital twin of cold metal transfer welding based on twin data and improved C-DCGAN algorithm

  • 期刊论文

  • 发表日期：

    2026年1月12日

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  • Xiangxing Li,
  • Kai Yang,
  • Haisong Huang,
  • Jiadui Chen,
  • Jinwei Yang

摘要

The formation quality of welded joints is influenced by the dynamic behavior of the molten pool, but existing visual sensing technologies are unable to directly measure its internal physical properties. Although numerical simulations play an important role in characterizing the welding formation process, their low computational efficiency and long processing time create a bottleneck in application. To address this, the study proposes an approach that integrates digital twins with numerical simulations to achieve precise and efficient prediction of this process. Firstly, a high-fidelity cold metal transfer welding digital twin model was developed on a finite element simulation platform, encompassing the geometry, physics, behavior, and rules of the welding formation process, and its reliability was verified through simulations of stainless steel welding joints. Subsequently, an improved conditional deep convolutional generative adversarial network (C-DCGAN) was designed as a lightweight surrogate model, incorporating affine mapping and residual structures, which efficiently replaced traditional numerical simulations while ensuring high accuracy and stability. The results show that the improved C-DCGAN model achieves higher predictive accuracy and efficiency than the comparative models at all three welding speeds. At a welding speed of 10 mm/s, the mean value of the MSE for molten pool image generation is as low as 0.0059, with good robustness. Meanwhile, the prediction time of the model requires only 5.44 s, significantly reducing the computation time compared to traditional numerical simulations and the comparative models. This study provides a foundation for the implementation of digital twin systems and advanced manufacturing quality control in industrial settings.