Enhancing 3D Printing Copper-PLA Composite Fabrication via Fused Deposition Modeling through Statistical Process Parameter Study

Abstract

The rapid advancement of additive manufacturing (AM) technologies has provided new avenues for creating three-dimensional (3D) parts with intricate geometries. Fused Deposition Modeling (FDM) is a prominent technology in this domain, involving the layer-by-layer fabrication of objects by extruding a filament comprising a blend of polymer and metal powder. This study focuses on the FDM process using a filament of Copper–Polylactic Acid (Cu-PLA) composite, which capitalizes on the advantageous properties of copper (high electrical and thermal conductivity, corrosion resistance) combined with the easily processable thermoplastic PLA material. The research delves into the impact of FDM process parameters, specifically, infill percentage (IP), infill pattern (P), and layer thickness (LT) on the maximum failure load (N), percentage of elongation at break, and weight of Cu-PLA composite filament-based parts. The study employs the response surface method (RSM) with Design-Expert V11 software. The selected parameters include infill percentage at five levels (10, 20, 30, 40, and 50%), fill patterns at five levels (Grid, Triangle, Tri-Hexagonal, Cubic-Subdivision, and Lines), and layer thickness at five levels (0.1, 0.2, 0.3, 0.4, and 0.5 mm). Also, the optimal factor values were obtained. The findings highlight that layer thickness and infill percentage significantly influence the weight of the samples, with an observed increase as these parameters are raised. Additionally, an increase in layer thickness and infill percentage corresponds to a higher maximum failure load in the specimens. The peak maximum failure load (230 N) is achieved at a 0.5 mm layer thickness and Tri-Hexagonal pattern. As the infill percentage changes from 10% to 50%, the percentage of elongation at break decreases. The maximum percentage of elongation at break is attained with a 20% infill percentage, 0.2 mm layer thickness, and 0.5 Cubic-Subdivision pattern. Using a multi-objective response optimization, the layer thickness of 0.152 mm, an infill percentage of 32.909%, and a Grid infill pattern was found to be the best configuration.