Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks

Abstract

Bio-based alternatives for conventional rigid foams have proven to be good substituents owing to their enhanced sustainability and competitive performance. However, because their manufacturing processes are complex and destructive testing is often impractical, this study investigates whether microstructural features can be correlated with mechanical properties in lignin-containing rigid polyurethane (PU) foams using machine-learning approaches. Various types and percentages of lignin-based polyols were investigated as a partial polyol replacement. Scanning electron microscopy (SEM) images and corresponding mechanical compression data were used to train a custom state-of-art dual-head convolutional neural network (CNN) targeting the specific prediction of density, specific compression modulus, specific yield strength, and specific compression strength. The CNN was optimized with a weighted multi-output loss function, achieving strong predictive performance R2 values ranging from 0.850 to 0.91 and correlation coefficients above 0.92 while maintaining mean absolute error percentages below 9%. This proves the trained network capability to predict and capture morphological features governing load bearing responses. On the other hand, Grad-CAM visualization revealed that the network focused its predictions on physically meaningful microstructural regions such as cell walls and strut junctions, which confirm that the proposed network can be classified as an interpretable, non-destructive and data-driven framework for predicting and understanding bio-based PU foams mechanical behavior, hence reducing the inconvenience caused by time consuming manufacturing and destructive testing.