A multiscale deep learning model for elastic properties of woven composites

• Time-consuming and costly computational analysis expresses the need for new methods for generalizing multiscale analysis of composite materials. Combining neural networks and multiscale modeling is favorable for bypassing expensive lower-scale material modeling, and accelerating coupled multi-scale analyses (FE2). In this work, neural networks are used to replace the time-consuming micromechanical finite element analysis of unidirectional composites, representing the local material properties of yarns in woven fabric composites in a multiscale framework. Leveraging the fast multiscale data generation procedure, we presented a second neural networks model to estimate the elastic engineering coefficients of a particular weave architecture based on a broad range of dry resin and fiber properties and yarn fiber volume fraction. As an outcome, this paper provides the user with a generalized, neural network-based approach to tackle the balance of computational efficiency and accuracy in the multiscale analysis of elastic woven composites.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Materialteknik -- Kompositmaterial och -teknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Materials Engineering -- Composite Science and Engineering (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering (hsv//eng)

Nyckelord

Artificial neural networks

Elastic properties

Multiscale analysis

Woven composites

Multiscale analysis

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