RESUMO
Polyolefins exhibit robust mechanical and chemical properties and can be applied in the medical field, e.g. for the manufacturing of dentures. Despite their wide range of applications, they are rarely used in extrusion-based printing due to their warpage tendency. The aim of this study was to investigate and reduce the warpage of polyolefins compared to commonly used filaments after additive manufacturing (AM) and sterilization using finite element simulation. Three types of filaments were investigated: a medical-grade polypropylene (PP), a glass-fiber reinforced polypropylene (PP-GF), and a biocopolyester (BE) filament, and they were compared to an acrylic resin (AR) for material jetting. Square specimens, standardized samples prone to warpage, and denture bases (n = 10 of each group), as clinically relevant and anatomically shaped reference, were digitized after AM and steam sterilization (134 °C). To determine warpage, the volume underneath the square specimens was calculated, while the deviations of the denture bases from the printing file were measured using root mean square (RMS) values. To reduce the warpage of the PP denture base, a simulation of the printing file based on thermomechanical calculations was performed. Statistical analysis was conducted using the Kruskal-Wallis test, followed by Dunn's test for multiple comparisons. The results showed that PP exhibited the greatest warpage of the square specimens after AM, while PP-GF, BE, and AR showed minimal warpage before sterilization. However, warpage increased for PP-GF, BE and AR during sterilization, whereas PP remained more stable. After AM, denture bases made of PP showed the highest warpage. Through simulation-based optimization, warpage of the PP denture base was successfully reduced by 25%. In contrast to the reference materials, PP demonstrated greater dimensional stability during sterilization, making it a potential alternative for medical applications. Nevertheless, reducing warpage during the cooling process after AM remains necessary, and simulation-based optimization holds promise in addressing this issue.