A Regression-Based Predictive Model Hierarchy for Nonwoven Tensile Strength Inference

Antweiler, Dario and Burgard, Jan Pablo and Harmening, Marc and Marheineke, Nicole and Schmeißer, Andre and Wegener, Raimund and Welke, Pascal (2025) A Regression-Based Predictive Model Hierarchy for Nonwoven Tensile Strength Inference. In: Informed Machine Learning :. Springer, Cham, pp. 63-90. ISBN 9783031830969

Abstract

Nonwoven materials, characterized by a random fiber structure, are essential for various applications including insulation and filtering. An industrial long-term goal is to establish a framework for the simulation-based design of nonwovens. Due to the random structures, simulations of material properties on fiber network level are computational expensive. We propose a predictive model hierarchy for inferring an important material property---the nonwoven tensile strength behavior. The model hierarchy is built using regression-based approaches, including linear and polynomial models, which provide interpretable results. This allows for significant speedup (six orders of magnitude) over the conventional simulations, while achieving good prediction results (R2=0.95R^2=0.95). The proposed models open the application to nonwoven material design, as they provide accurate and cost-effective surrogates for predicting material properties. In this way, our work serves as a proof of concept.