CNN-based prediction of microstructure-derived random property fields of composite materials

The simulation of random spatial variation in the mechanical properties of composite materials using random fields derived from their microstructure can be computationally demanding, since it often requires numerical homogenization of a large number of stochastic volume elements (SVEs). These SVEs a...

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Bibliographic Details
Published in:Computer methods in applied mechanics and engineering 2024-10, Vol.430, p.117207, Article 117207
Main Authors: Gavallas, Panagiotis, Stefanou, George, Savvas, Dimitrios, Mattrand, Cécile, Bourinet, Jean-Marc
Format: Article
Language:English
Subjects:
CNN
Composite material
Mechanical properties
Random fields
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Summary:The simulation of random spatial variation in the mechanical properties of composite materials using random fields derived from their microstructure can be computationally demanding, since it often requires numerical homogenization of a large number of stochastic volume elements (SVEs). These SVEs are usually extracted from an initial image of the composite microstructure by using a moving window technique and their homogenized properties constitute the discrete data points of the random field at the centroid of each SVE. By replacing the expensive and highly repetitive homogenization procedure with a convolutional neural network (CNN), it is possible to perform nearly instant computations of random property fields. In this work, a CNN is proposed, that takes as input an SVE image and returns its apparent mechanical properties, and can therefore be applied along with the moving window technique. Training is performed on randomly generated SVEs with varying volume fractions and inclusion positions, which are representative of the SVEs obtained during processing of the initial large composite image. It is shown that the proposed CNN can accurately predict the mechanical properties of SVEs and it is subsequently applied for the prediction of random property fields derived from computer simulated and real microstructure images. Results show that, with the proposed methodology, it is possible to make accurate predictions of random fields within a few seconds, a procedure which could potentially require hours of computation time with the finite element based approach.
ISSN:0045-7825
DOI:10.1016/j.cma.2024.117207