posted on 2023-11-30, 17:06authored byRodrigo
Q. Albuquerque, Florian Rothenhäusler, Philipp Gröbel, Holger Ruckdäschel
In this work, petroleum-based epoxy resins and curing
agents are
mixed with their biobased counterparts to create sustainable epoxy
resin systems for resin transfer molding techniques. Multiobjective
Bayesian optimization (BO) was employed to simultaneously maximize
two mechanical and one thermal property of eight-component, biobased
thermosets with as few as five additional experiments, enhancing sustainability
by reducing resource-intensive trials. Machine learning (ML) models
were used for property prediction based on the formulation composition.
The LASSO model provided interpretable results, revealing relationships
between specific components and target properties, besides exhibiting
prediction accuracy of ca. 94%. This research highlights the potential
of multiobjective BO in designing sustainable biobased epoxy resin
systems and emphasizes the interpretability and predictive power of
ML models in material formulation optimization, contributing to environmentally
friendly and cost-effective material development.