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Carbon emission quantification, modeling, and optimization in additive manufacturing : a case of material and energy consumption reduction in fused filament fabrication
Journal article   Open access   Peer reviewed

Carbon emission quantification, modeling, and optimization in additive manufacturing : a case of material and energy consumption reduction in fused filament fabrication

Shailendra Pawanr and Kapil Gupta
Clean technologies, Vol.8(4), p.127
10/08/2026
Handle:
https://hdl.handle.net/10210/520990

Abstract

fused filament fabrication carbon emission quantification composite
Understanding the carbon emission characteristics of fused filament fabrication (FFF) is important for the development of more sustainable additive manufacturing practices. This study presents a framework for quantifying, modelling, and optimizing the carbon emissions of FFF-printed specimens of carbon-reinforced Polyethylene Terephthalate Glycol (PETG-CF) composite. Carbon emissions were assessed within a cradle-to-gate system boundary by considering material consumption and electrical energy usage during fabrication. The influence of print speed, raster angle, layer height, infill density and infill pattern on carbon emissions were experimentally investigated. Response Surface Methodology (RSM) was utilized to formulate a predictive carbon emission model, while analysis of variance was applied to assess the significance of the process parameters. The findings revealed that infill density, infill pattern, layer height, and raster angle significantly affected carbon emissions, while print speed showed a comparatively lower influence. Contour plot analysis was used to visualize parameter interactions and identify low-emission regions. RSM-based optimization predicted a minimum carbon emission of 0.0732 kgCO2eq at a print speed of 220 mm/s, layer height of 0.12 mm, infill density of 50%, raster angle of 0°, and rectilinear infill pattern. The proposed framework presents a practical strategy for integrating carbon emissions for a sustainable FFF process.
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Open Access CC BY V4.0
url
https://doi.org/10.3390/cleantechnol8040127View
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