Development of Prediction Method for Dimensional Stability of 3D-Printed Objects

Abstract

Fused deposition modeling (FDM), as one of the additive manufacturing processes, is known for strong layer adhesion suitable for prototypes and end-use items. This study used a multiple regression model and statistical analysis to explore the dimensional accuracy of FDM objects. Factors such as inclination angle, layer thickness, support space, and raster angle were examined. Machine learning models (Gaussian process regression (GPR), support vector machines (SVM), and artificial neural network (ANN)) predicted dimensions using 81 datapoints. The mean squared dimensional error (MSDE) between the measured and designed surface profiles was selected as an output for the dimensional accuracy. Support spacing, layer thickness, and raster angle were determined to be statistically significant, and all factors were confirmed as significant predictors. The coefficients of determination for multiple linear regression, GPR, SVM, and ANN models were 76%, 98%, 93%, and 99%, respectively. The mean absolute errors (MAEs)—errors between the measured and the predicted MSDEs—were 0.020 mm and 0.034 mm, respectively, for GPR and SVM models. The MAEs for ANN models were 0.0055 mm for supporting cases and 2.1468 × 10−5 mm for non-supporting cases.

Keywords

• fused deposition modeling
• multiple regression model
• ANOVA
• artificial neural network