论文标题
一项关于使用基于图像的机器学习方法开发邮票形成模拟的替代模型的研究
A study on using image based machine learning methods to develop the surrogate models of stamp forming simulations
论文作者
论文摘要
在金属形成的设计优化中，使用替代模型来分析有限元分析（FEA）模拟越来越重要。但是，使用基于标量的机器学习方法（SBMLMS）的传统替代模型缺乏准确性和可推广性。这是因为SBMLM无法利用模拟的位置信息。为了克服这些缺点，本文利用了基于图像的机器学习方法（IBMLMS）。支持IBMLM优势的位置信息的基本理论是定性解释的。基于该理论，开发了RES-SE-U-NET IBMLM替代模型，并将其与多层感知器（MLP）作为引用SBMLM替代模型进行了比较。证明IBMLM模型在准确性，可推广性，鲁棒性和信息性方面比MLP SBMLM模型具有优势。本文提出了一种有前途的方法，即在替代模型中利用IBMLM，以最大程度地利用FEA结果中的信息。还讨论了受本文启发的未来前瞻性研究。
In the design optimization of metal forming, it is increasingly significant to use surrogate models to analyse the finite element analysis (FEA) simulations. However, traditional surrogate models using scalar based machine learning methods (SBMLMs) fall in short of accuracy and generalizability. This is because SBMLMs fail to harness the location information of the simulations. To overcome these shortcomings, image based machine learning methods (IBMLMs) are leveraged in this paper. The underlying theory of location information, which supports the advantages of IBMLM, is qualitatively interpreted. Based on this theory, a Res-SE-U-Net IBMLM surrogate model is developed and compared with a multi-layer perceptron (MLP) as a referencing SBMLM surrogate model. It is demonstrated that the IBMLM model is advantageous over the MLP SBMLM model in accuracy, generalizability, robustness, and informativeness. This paper presents a promising methodology of leveraging IBMLMs in surrogate models to make maximum use of info from FEA results. Future prospective studies that inspired by this paper are also discussed.
