We are pleased to announce the publication of a new scientific article on Foam Additive Manufacturing in Materials & Design by Elsevier:
“Data-driven process maps for functionally graded foams in Foam Additive Manufacturing: Accuracy and interpretability across six models”
The paper presents a data-driven approach to predict and control foam density in FAM, based on a dataset of 528 PLA–CO₂ experiments. By comparing six predictive models, the study shows how process maps and machine learning can support the development of lightweight, mono-material and functionally graded 3D printed structures.
This publication further strengthens Oniro’s scientific roadmap toward advanced FAM software, digital process navigation and industrial applications where weight reduction, material efficiency and local mechanical tuning are key.
