Regression Modeling and Optimization of CNC Milling Parameters for FDM-Printed TPU 95A Components

Additively manufactured thermoplastic polyurethane (TPU 95A) is widely used in engineering, yet its machining behavior remains insufficiently explored. This study investigates the post-processing machinability of FDM-fabricated TPU 95A using CNC milling, with a particular focus on material removal rate (MRR) and surface roughness (Ra). A full factorial design of experiments (81 runs) is conducted, considering four input parameters such as spindle speed (N; 2000, 4000, 6000 rpm) and feed rate (F; 100, 200, 300 mm/min) on the CNC vertical machining center, together with infill density (ϕ; 33%, 66%, 100%) and layer thickness (LT; 1.0, 1.5, 2.0 mm). MRR is modeled and optimized across all densities, achieving strong fit (R² = 0.94; Adj-R² = 0.93). The optimum conditions are found to be MRR ≈ 1251 mm³/min at F = 300 mm/min, ϕ = 100%, N ≈ 3500 rpm and LT ≈ 1.05 mm. Ra can only be measured for 100% infill specimens, as lower infill surfaces violate profile measurement requirements. Its regression model shows weak explanatory power (R² = 0.14; Adj-R² = 0.03) and is excluded from optimization. Instead, Ra is reported descriptively: milling reduced roughness from ≈25-30 μm (as-printed) to ≈13.8 μm under favorable conditions. Overall, the study highlights machining's role in the hybrid manufacturing practice.