Abstract
Efficient deployment of the right materials at the right place is a nontrivial problem. Structural components can achieve superior mechanical properties through tunable material deployment. However, such a structure-property-performance relationship is not explicit and requires the trial-and-error procedure. Discovering this relationship is extremely challenging due to a large number of possible material microstructures and deployed position combinations, and the search for the optimal design is computationally expensive. In this study, a data-driven approach is proposed to efficiently discover the underlying relationship between the deployment of a specific material microstructure and the final structural performance. The uniaxial tension of a perforated composite plate was used as a demonstrator. A high-fidelity representative volume element of a unidirectional fiber reinforced composite was constructed and utilized to calibrate the phenomenological constitutive model. A data compression algorithm was applied to the structure geometry in a finite element simulation to mechanically group similar regions and reduce dimensionality. After optimization, the calculated scenarios were selected based on the target performance. High-efficiency multiscale analysis was subsequently conducted on the selected conceptual designs to reveal the micro-deformation mechanism and interpret the interaction between the fibers and the matrix.
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