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September 26, 2025Micromachines2 citationsOpen Access

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

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KEKaan Emre EnginZÇZihni Alp Çevik

Key Points

  • Optimal conditions achieve a material removal rate of about 1251 mm3/min with a feed rate of 300 mm/min.
  • The study finds surface roughness improvement from approximately 25–30 μm to 13.8 μm at optimal milling settings.
  • A full factorial design of experiments was conducted with 81 runs examining critical CNC parameters.
  • The regression model for surface roughness was found to have weak explanatory power, leading to its exclusion from optimization.

Abstract

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.

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Cite This Study

Engin et al. (2025) studied this question.

synapsesocial.com/papers/68d6cd5bb1249cec298b3236https://doi.org/10.3390/mi16101078
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