The tuning of fused filament fabrication parameters is notoriously challenging. We propose an autonomous data-driven method to select parameters based on in situ measurements. We use a laser sensor to evaluate the surface roughness of a printed part. We then correlate the roughness to the mechanical properties of the part, and show how print quality affects mechanical performance. Finally, we use Bayesian optimization to search for optimal print parameters. We demonstrate our method by printing liquid crystal polymer samples, and successfully find parameters that produce high-performance prints and maximize the manufacturing process efficiency.
@article{arxiv.2210.15239,
title = {Data-Driven Process Optimization of Fused Filament Fabrication based on In Situ Measurements},
author = {Xavier Guidetti and Marino Kühne and Yannick Nagel and Efe C. Balta and Alisa Rupenyan and John Lygeros},
journal= {arXiv preprint arXiv:2210.15239},
year = {2022}
}