With the increase of complexity of Computer-Aided Engineering (CAE) models and practical problems, the evaluation cost of sheet forming simulation is commonly expensive. Surrogate models have been employed for efficient evaluations but trouble the problem of inverse scattering. Moreover, the accuracy of the forming evaluation based on the widely used Forming Limit Diagram (FLD) is influenced by the non-working regions. In this study, image-processing techniques are employed. Simultaneously, two novel image-driven Generative Inverse Networks (GINs) are proposed to improve the sheet-forming design's efficiency and accuracy. Through validations, GIN Version d (GIN-V_d) is more efficient and can obtain higher accuracy. However, because the desired optimum should be given in advance, such applications might be limited. In comparison, the GIN Version g (GIN-V_g) is more flexible.

随着计算机辅助工程（CAE）模型的复杂性和实际问题的增加，板材成形模拟的评估成本通常很高。代理模型已被用于有效评估，但会遇到逆散射问题。此外，基于广泛使用的成形极限图（FLD）的成形评估的准确性受到非工作区域的影响。在这项研究中，采用了图像处理技术。同时，提出了两种新颖的图像驱动生成逆网络（GIN）来提高片材成型设计的效率和准确性。通过验证，GIN 版本d (GIN-V _d) 效率更高，可以获得更高的准确率。但是，由于应预先给出所需的最佳值，因此此类应用可能会受到限制。相比之下，GIN 版本g (GIN-V _g ) 更加灵活。