• Optimal configuration achieved energy reduction of 38. 5% (0. 16 kWh/part), cost reduction of 27. 9% (R 0. 80/part), and CO 2 emissions reduction of 38. 4% (0. 00638 kg/part). • Print speed exerted the strongest influence on energy consumption (50% reduction from 20 to 50 mm/s). • Layer height and infill density critically affected capability indices (C gk improvements of 71. 4% and 22. 7%, respectively). • Normalized Global Criterion optimized 6 responses without quality loss • Multi-objective optimization integrates sustainability gains (energy, cost, CO 2) with quality metrics (C gk ≥ 1. 33, C a ≤ 1. 00). Contemporary manufacturing demands simultaneous optimization of economic, environmental, and quality objectives. This study addresses multi-objective optimization in Fused Filament Fabrication additive manufacturing using Polylactic Acid, integrating energy efficiency, production cost, measurement system capability, and dimensional accuracy. A Central Composite Design experimental arrangement evaluated four input variables: Layer Width, Layer Height, Infill Density, and Print Speed. Response Surface Methodology with second-order polynomial models quantified individual and interaction effects, while the Normalized Global Criterion method enabled simultaneous optimization of six responses. Quantitative results demonstrated that print speed exerted the strongest influence on energy consumption (50% reduction from 20 to 50 mm/s), while layer height and infill density critically affected measurement capability indices (C gk improvements of 71. 4% and 22. 7%, respectively). The optimal configuration (LW = 0. 59 mm, LH = 0. 12 mm, ID = 10%, S = 50 mm/s) achieved energy reduction of 38. 5% (0. 16 kWh/part), cost reduction of 27. 9% (R 0. 80/part), CO 2 emissions reduction of 38. 4% (0. 00638 kg/part), capability indices exceeding 1. 33 (C gk;w = 1. 62, C gk;t = 1. 39), and dimensional accuracy within ± 0. 20 mm tolerances (C a;w = 0. 64, C a;t = 1. 00). Therefore, this integrated multi‑objective model for FFF advances smart, sustainable, and resilient manufacturing by unifying data‑driven process modeling, environmental metrics, and metrological reliability in a single decision‑support framework.
Almeida et al. (Sun,) studied this question.