Data-Driven Process Optimization of Fused Filament Fabrication based on In Situ Measurements

TL;DR

This paper presents an autonomous, data-driven approach for optimizing fused filament fabrication parameters using in situ laser measurements, Bayesian optimization, and correlation analysis to improve print quality and mechanical performance.

Contribution

It introduces a novel method combining in situ surface roughness measurement, correlation with mechanical properties, and Bayesian optimization for process parameter tuning in 3D printing.

Findings

Successfully optimized parameters for high-performance liquid crystal polymer prints

Demonstrated improved manufacturing efficiency and print quality

Established correlation between surface roughness and mechanical strength

Abstract

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.