Foam property prediction and inception via a hybrid machine learning and model order reduction framework

Abstract

Novel methodologies in machine learning enable non-destructive prediction of mechanical properties through the use of either material components or material imaging. Predictive technologies are continuously being developed in the field of mechanical and materials engineering. However, when it comes to inverse problems, the identification of the optimal microstructural content to create the material with the desired properties is often complicated and ill posed. In fact, the inverse problem is not unique, and multiple material combinations can lead to the desired properties. Working with biobased materials adds a layer of complexity to the inverse problem since in most cases biosourced components are mixtures rather than pure components. This work establishes a methodology for the prediction of material properties based on imaging of lignin-containing polyurethane rigid foams and solving the inverse problem by generating the required lignin-based formulation leading to the desired mechanical properties. The methodology employs a novel learnable kernel principal component analysis, based on the combination of the non-linear model order reduction technique with machine learning autoencoders mapping the physical data into a large dimensional space. The results are material generation through required mechanical properties, mechanical properties identification from microstructure imaging, microstructure generation from mechanical properties, and (bio)chemical combinations. The predictions of the mechanical properties are within less than 2.3% on average from the actual properties on both training and testing sets, while the inception shows an accuracy of 0.893 on the train set and 0.891 on the test set.