In design for forming, it is becoming increasingly significant to develop surrogate models of high-fidelity finite element analysis (FEA) simulations of forming processes, to achieve effective component feasibility assessment as well as process and component optimizations. However, surrogate models using traditional scalar-based machine learning methods (SBMLMs) fall short on accuracy and generalizability. This is because SBMLMs fail to harness the location information available from the simulations. To overcome this shortcoming, the theoretical feasibility and practical advantages of innovatively applying image-based machine learning methods (IBMLMs) in developing surrogate models of sheet stamp forming simulations are explored in this study. To demonstrate the advantages of IBMLMs, the effect of the location information on both design variables and simulated physical fields is firstly proposed and analyzed. Based on a sheet steel stamping case study, a Res-SE-U-Net IBMLM surrogate model of stamping simulations is then developed and compared with a baseline multi-layer perceptron (MLP) SBMLM surrogate model. The results show that the IBMLM model is advantageous over the MLP SBMLM model in accuracy, generalizability, robustness, and informativeness. This paper presents a promising methodology in leveraging IBMLMs as surrogate models to make maximum use of information from stamp forming FEA results. Future prospective studies that are inspired by this paper are also discussed.

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In stress field analysis, the finite element method is a crucial approach, in which the mesh-density has a significant impact on the results. High mesh density usually contributes authentic to simulation results but costs more computing resources. To eliminate this drawback, we propose a data-driven mesh-density boost model named SuperMeshingNet that uses low mesh-density as inputs, to acquire high-density stress field instantaneously, shortening computing time and cost automatically. Moreover, the Res-UNet architecture and attention mechanism are utilized, enhancing the performance of SuperMeshingNet. Compared with the baseline that applied the linear interpolation method, SuperMeshingNet achieves a prominent reduction in the mean squared error (MSE) and mean absolute error (MAE) on the test data. The well-trained model can successfully show more excellent performance than the baseline models on the multiple scaled mesh-density, including 2X, 4X, and 8X. Enhanced by SuperMeshingNet with broaden scaling of mesh density and high precision output, FEA can be accelerated with seldom computational time and cost.

Organic–inorganic halide two-dimensional (2D) layered perovskites have been demonstrated to have better environmental stability than conventional three-dimensional perovskites. In this study, we investigate the fabrication of electron transport layer (ETL)-free Ruddlesden–Popper 2D perovskite solar cells (PSCs) by tuning the work function of a fluorine-doped tin oxide (FTO) electrode. With the deposition of polyethylenimine (PEIE) onto its surface, the work function of the FTO electrode could be raised from −4.72 to −4.08 eV, which is more suitable for electron extraction from the perovskite absorber. Using this technique, the ETL-free 2D PSCs exhibited an excellent power conversion efficiency (PCE) of 12.7% (on average), which is substantially higher than that of PSCs fabricated on a pristine FTO electrode (9.6%). Compared with the PSCs using TiO2, the ETL-free PSCs could be fabricated under a low processing temperature of 100 °C with excellent long-term stability. After 15 days, the FTO/PEIE-based ETL-free PSCs showed a PCE degradation of 16%, which is significantly lower than that of the TiO2-based case (29%). The best-performing PSC using a FTO/PEIE cathode showed a high PCE of 13.0%, with a small hysteresis degree of 2.3%.