Integrating Taguchi and Desirability Function Analysis to Practice Low Carbon Fabrication With Recycled Filament

ABSTRACT This study presents an in‐depth investigation of the multi‐objective optimization of recycled polylactic acid (rPLA) for 3D printing via fused deposition modeling (FDM) with the aim of balancing industrial accuracy, surface quality, and environmental sustainability. Diverging from traditional literature that focuses on limited process parameters, this research applies a design of experiments (DoE) approach combining Taguchi and desirability function analysis (DFA) to evaluate 10 high‐dimensional parameters while incorporating carbon price as a critical optimization metric. The results indicate that layer height and build orientation are the primary drivers of the accuracy, roughness, and carbon price. Although single‐objective accuracy optimization triggers a 412.5% cost increase for marginal accuracy gains, multi‐objective Scenario 3 offers a holistic equilibrium, incurring a minor 6.25% cost increase to achieve reductions of 46.03% in roughness and 1.50% in absolute geometric error. This study addresses existing research gaps regarding geometric tolerance control, surface topography, and low‐carbon manufacturing decision models for rPLA, providing a quantitative foundation and an innovative pathway for a green transition in additive manufacturing.