Characterisation of print path deviation in material extrusion

Abstract

Abstract Material extrusion, also known as fused filament fabrication, is one of the most popular additive manufacturing techniques due to its lower cost and ease of handling. However, parts produced by material extrusion have relatively poor mechanical performance, dimensional accuracy and thermal performance as compared to parts produced by subtractive manufacturing due to high void content. Previous studies have suggested print path deviation as the cause of these voids, but no attempt has yet been made to characterise these deviations. In this study, we propose a method to assess print path deviation for material extrusion that may reduce the formation of voids in printed parts. Geometric features including straight paths, various angled corners and curves of varying radii are printed at different print speeds using an open-source printer and then imaged under a microscopic. The deviation in print path centroid and width is classified as being a combination of systematic and stochastic deviations. Systematic deviation is determined by the difference between the mean of the actual print path and the ideal print path sent to the printer by the user. Stochastic deviation represents the randomness across print samples and is given by the root mean square deviation. The relationship between stochastic deviation between any two points along the print path is determined by a correlation coefficient. The results show that both print speed and different geometric print features affect the amount of deviation in the print path. In the case of correlation of the stochastic deviation along print paths, geometric features are found to have a much greater effect than print speed. The proposed method provides a low cost and highly transferrable technique to characterise the print path deviation within material extrusion parts with respect to varying printing parameters. An accurate understanding of local print deviations within a part plays a major role in the analysis and topology optimisation of 3D printed parts and gives the ability to assess the print quality and identify the root cause of print deviations, thus reducing voids and improving mechanical performance, dimensional accuracy and thermal properties of the printed part.