Conditional diffusion model for inverse prediction of process parameters and dendritic microstructures from mechanical properties.

ABSTRACT: In this study, we develop a conditional diffusion model that proposes the optimal process parameters and predicts the microstructure for the desired mechanical properties. In materials development, it is costly to try many samples with different parameters in experiments and numerical simulations. The use of data-driven inverse design method can reduce the cost of materials development. This study develops an inverse analysis model that predicts process parameters and microstructures. This method can be used for any material, but in this study it is applied to polymeric material, which is the matrix resin of carbon fiber reinforced thermoplastics as an example. Matrix resins contain a mixture of dendrites, which are crystalline phases, and amorphous phases even after crystal growth is comp

SUBMITTER: Ikeda A 

PROVIDER: S-EPMC12549910 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature