Examination of 3D printing parameters using machine learning

In this study, the mechanical properties of tensile samples produced in 3D printers with the fused deposition method (FDM) were investigated. Here, the parameters such as layer (filament) thickness, infill type and support angle in the FDM method were examined. The production was produced with Up-right and edge directions. As a result of the experiments, the best layer thickness in terms of tensile strength was 0.09 mm, and the infill type was full infill type, while different results were obtained in the support angle. According to the variance analysis (ANOVA) values, it was observed that the layer thickness and infill type were quite effective on the tensile strength, but the support angle was at a negligible level. In the second stage, the results were estimated with xgboost and catboost from the machine learning algorithms and linear regression models. The most effective algorithm on the examined mechanical properties was determined as the catboost algorithm.