Data-Driven Microstructure Property Relations
This work addresses the challenge of material property prediction for researchers in materials science and engineering, but it is incremental as it builds on existing methods like POD and neural networks.
The paper tackles the problem of predicting effective heat conductivity for heterogeneous microstructured materials using image data, achieving this by employing unsupervised and supervised machine learning methods, including incremental snapshot POD and neural networks, with numerical examples demonstrating prediction accuracy.
An image based prediction of the effective heat conductivity for highly heterogeneous microstructured materials is presented. The synthetic materials under consideration show different inclusion morphology, orientation, volume fraction and topology. The prediction of the effective property is made exclusively based on image data with the main emphasis being put on the 2-point spatial correlation function. This task is implemented using both unsupervised and supervised machine learning methods. First, a snapshot proper orthogonal decomposition (POD) is used to analyze big sets of random microstructures and thereafter compress significant characteristics of the microstructure into a low-dimensional feature vector. In order to manage the related amount of data and computations, three different incremental snapshot POD methods are proposed. In the second step, the obtained feature vector is used to predict the effective material property by using feed forward neural networks. Numerical examples regarding the incremental basis identification and the prediction accuracy of the approach are presented. A Python code illustrating the application of the surrogate is freely available.