Machine Learning-Based Prediction of Surface Roughness in Fused Filament Fabrication

This study presents a machine learning-based approach for predicting surface roughness in Fused Filament Fabrication (FFF) 3D printing, focusing on two key parameters: arithmetic average roughness (Ra) and mean peak-to-valley height (Rz). Experimental data were collected for two materials—Polylactic Acid (PLA) and Thermoplastic Polyurethane (TPU)—under varied printing parameters including nozzle temperature, deposition thickness, and print speed. The data was collected in the form of tables for both the materials and then the tables were combined. Multiple supervised learning models, including Polynomial Regression, Lasso Regression, Random Forest, Support Vector Regression, and XGBoost, were developed and compared. RMSE (Root Mean Square Error) and R² score were used as evaluation parameters. Additionally, a stacked ensemble learning strategy was implemented, exploiting the correlation between Ra and Rz for enhanced predictive accuracy. Results demonstrate that the ensemble approach significantly outperforms individual models, achieving near-perfect R² scores when incorporating spatial measurement line data. This work contributes to improved process optimization and quality control in FFF, enabling more reliable surface finish prediction across different thermoplastic materials