Knowledge database creation for design of polymer matrix composite

We present a mechanistic data science (MDS) framework capable of building a composite knowledge database for composite materials design. The MDS framework systematically leverages data science to extract mechanistic knowledge from composite materials system. The composite response database is first generated for three matrix and four fiber combinations using a physics-based mechanistic reduced-order model. Next, the mechanistic features of the composites are identified by mechanistically analyzing the composites stress–strain responses. A relationship between the composite properties and the constituents’ material features are established through a mechanics constrained data science-based learning process after representing materials in latent space, following a dimension reduction technique. We demonstrate the capability of predicting a composite materials system for target properties (material elastic properties, yield strength, resilience, toughness, and density) from the MDS created knowledge database. The MDS model is predictive with reasonable accuracy, and capable of identifying the materials system along with the tuning required to achieve desired composite properties. Development of such MDS framework can be exploited for fast materials system design, creating new opportunity for performance guided materials design.