Influence of Natural and Synthetic Reinforcements on the Volumetric and Surface Roughness Properties of 3D Printed Biodegradable Polymer

Abstract

This study investigates the influence of key Fused Deposition Modeling (FDM) process parameters layer thickness (LT), extrusion temperature (ET), infill density (IF), and extrusion width (EW) on the dimensional accuracy and surface finish of 3D printed parts fabricated from three biodegradable materials: Polylactic Acid (PLA), PLA-Wood, and PLA-Carbon Fiber (PLACF). A Central Composite Rotatable Design (CCRD) was employed to conduct 31 experimental trials per material, generating a comprehensive dataset for modeling and analysis. Volumetric deviation (ΔV) and surface roughness (Ra) were selected as primary response variables, with their behavior modeled using second-order polynomial regression equations. Statistical significance and contribution of individual parameters and their interactions were determined through Analysis of Variance (ANOVA). For PLA, LT was identified as the most dominant parameter affecting ΔV, contributing 66.42% of total variation, while EW exhibited the highest effect in PLA-CF at 39% (via IF×EW interaction). PLA-Wood exhibited greater variability, with LT contributing 33.96% to ΔV. In the case of Ra, LT dominated PLA (78.13%), while ET×LT and EW×IF were the most significant interactions in PLA-Wood and PLA-CF, contributing 35.38% and 23.7% respectively. Among the three materials, PLA-CF showed the least ΔV range (76.25 mm³) and a moderate Ra range (9.6 µm), confirming its superior dimensional stability and smoother deposition. Conversely, PLA-Wood exhibited the highest ΔV range (278.87 mm³) and surface variability due to its hygroscopic nature and heterogeneous structure. The developed regression models demonstrated high predictive accuracy, with R² values exceeding 0.99 for both ΔV and Ra across all materials. Model predictions were validated via confirmation tests, yielding results within 95% confidence intervals and maximum prediction errors of ±0.22 µm for Ra and ±7.80 mm³ for ΔV in PLA, ±0.77 µm for Ra and ±7.5 mm³ for ΔV in PLA-Wood, and ±0.48 µm and ±3.38 mm³ for PLA-CF. These findings highlight the critical role of thermal and geometric process parameters in determining print quality and offer a robust optimization framework for achieving enhanced dimensional accuracy and surface quality in reinforced biodegradable polymers. This study provides a validated foundation for future research aimed at developing high-precision, eco-friendly components for functional and load-bearing applications. Keywords: Additive Manufacturing, FDM, PLA, PLA-Wood, PLA-CF, Volumetric Shrinkage, Surface Roughness, Process Optimization, ANOVA, CCRD