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In the design optimization of metal forming, it is increasingly significantto use surrogate models to analyse the finite element analysis (FEA)simulations. However, traditional surrogate models using scalar based machinelearning methods (SBMLMs) fall in short of accuracy and generalizability. Thisis 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 onthis theory, a Res-SE-U-Net IBMLM surrogate model is developed and comparedwith a multi-layer perceptron (MLP) as a referencing SBMLM surrogate model. Itis demonstrated that the IBMLM model is advantageous over the MLP SBMLM modelin accuracy, generalizability, robustness, and informativeness. This paperpresents a promising methodology of leveraging IBMLMs in surrogate models tomake maximum use of info from FEA results. Future prospective studies thatinspired by this paper are also discussed.

A study on using image based machine learning methods to develop the surrogate models of stamp forming simulations