Data-efficient one-step mechanical design of composites using generative AI

Abstract

The distribution of material phases is crucial to determine the composite's mechanical properties. While the entire structure-mechanics relationship of highly ordered material distributions can be studied with a finite number of cases, this relationship is challenging to reveal for complex irregular distributions, preventing the design of such material structures from meeting specific mechanical requirements. The noticeable developments of artificial intelligence algorithms in material design enable the discovery of hidden structure-mechanics correlations, which is essential for designing composites of complex structures. It is intriguing how these tools can assist composite design. Here, we focus on the rapid generation of complex irregular composite structures and the stress distribution in loading. We find that generative AI, enabled through fine-tuned Low-Rank Adaptation models, can be trained with a few inputs to generate synthetic composite structures and the corresponding von Mises stress distribution. The results show that this technique is convenient in generating massive composite designs with useful mechanical information that dictates stiffness, fracture, and robustness of the material with one model, and such must be done by several different experimental or simulation tests. This research offers valuable insights for improving composite design to expand the design space and automatic screening of composite designs for improved mechanical functions.

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