Exploring nonlinear strengthening in polycrystalline metallic materials by machine learning methods and heterostructure design

To improve the strength and plasticity of structural materials, researchers often introduce various strengthening mechanisms such as second-phase strengthening, dislocation strengthening, and back stress strengthening (HDI). Due to the interaction of multiple mechanisms, the linear su-perposition relationship has a poor fitting effect and is only used for rough calculations of the strengthening mechanisms. In this study, the transfer learning data was used to optimize the deep learning network structure (Re-CNN) based on the residual algorithm, and the yield strength prediction physical neural informed model (PNIM) of polycrystalline metallic materials was established. To promote the industrial application of the heterostructure design method, a me-dium carbon steel heterostructure design strategy based on the existing equipment of the factory was proposed. Medium-carbon heterostructure materials (MHSM) with mixed strengthening mechanisms were successfully prepared. MHSM exhibits excellent comprehensive mechanical properties. When a linear relationship is used to describe the MHSM yield strength, there is a large error, while Re-CNN shows satisfactory prediction accuracy. The linear relationship is incom-patible with homogeneous structure materials and heterogeneous structure materials, and its universality is lower than that of nonlinear Re-CNN. Re-CNN shows high cross-scale prediction ability and can be compatible with homogeneous microstructures and heterogeneous micro-structures. Using the heterogeneity evolution characteristics of MHSM, the key factors deviating from the linear relationship were revealed. The overestimation and underestimation of the linear relation are demonstrated by Taylor factor and TEM analysis to be caused by the multiscale properties of ferrite, the behavior of the second phase particles, and the interaction of various mechanisms. This study provides a new idea for the cross-scale calculation of the mechanical properties of polycrystalline metallic materials.