Research on Material Stress Strain Curve Prediction Methods Based on Transfer Learning and Artificial Intelligence

The mechanical properties of materials play a crucial role in real life. However, methods to measure these properties are usually time-consuming and labour intensive. Small Punch Through (SPT) has non-destructive characteristics and can obtain load-displacement curves of specimens, but it cannot visually extract the mechanical properties of materials. Therefore, we designed a proprietary SPT experiment and fixture, built a finite element method (FEM) model and developed a multi-fidelity model capable of predicting the mechanical properties of steel and aluminium alloys. It makes use of multi-fidelity datasets obtained from SPT and FEM simulation experiments, and this integration allows us to support and optimize the predictive accuracy of the study, thus ensuring a comprehensive and reliable characterization of the mechanical properties of the materials. The model also takes into account variations in material thickness and can effectively predict the mechanical properties of materials with different thicknesses.