Nakka, R and Harursampath, D and Ponnusami, SA (2023) A generalised deep learning-based surrogate model for homogenisation utilising material property encoding and physics-based bounds. In: Scientific Reports, 13 (1).
|
PDF
sci_rep_13_1_2023.pdf - Published Version Download (7MB) | Preview |
Abstract
The use of surrogate models based on Convolutional Neural Networks (CNN) is increasing significantly in microstructure analysis and property predictions. One of the shortcomings of the existing models is their limitation in feeding the material information. In this context, a simple method is developed for encoding material properties into the microstructure image so that the model learns material information in addition to the structure-property relationship. These ideas are demonstrated by developing a CNN model that can be used for fibre-reinforced composite materials with a ratio of elastic moduli of the fibre to the matrix between 5 and 250 and fibre volume fractions between 25 and 75, which span end-to-end practical range. The learning convergence curves, with mean absolute percentage error as the metric of interest, are used to find the optimal number of training samples and demonstrate the model performance. The generality of the trained model is showcased through its predictions on completely unseen microstructures whose samples are drawn from the extrapolated domain of the fibre volume fractions and elastic moduli contrasts. Also, in order to make the predictions physically admissible, models are trained by enforcing Hashin�Shtrikman bounds which led to enhanced model performance in the extrapolated domain. © 2023, The Author(s).
| Item Type: | Journal Article |
|---|---|
| Publication: | Scientific Reports |
| Publisher: | Nature Research |
| Additional Information: | The copyright for this article belongs to authors. |
| Department/Centre: | Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering) |
| Date Deposited: | 20 Oct 2024 12:33 |
| Last Modified: | 20 Oct 2024 12:33 |
| URI: | http://eprints.iisc.ac.in/id/eprint/85356 |
Actions (login required)
 |
View Item |
