Affiliations:
Industrial Engineering Department, College of Engineering, Northern Border University, Arar, Saudi Arabia
Manufacturing processes are increasingly required to improve sustainability in order to reduce energy use and environmental impact. This study develops regression models to quantify and predict Specific Printing Energy (SPE) and CO₂ emissions in Fused Filament Fabrication (FFF) using Polylactic Acid (PLA) filament. The main objective is to optimize key printing parameters to support more efficient and sustainable additive manufacturing. A full factorial experimental design was applied, including 729 experimental runs based on six FFF parameters at three levels. Multiple linear regression models were developed using Minitab to evaluate the effects of these parameters on SPE and CO₂ emissions. Model reliability was confirmed through comprehensive statistical analysis, including analysis of variance (ANOVA), R-squared evaluation, and residual diagnostics. The results show that the models explain 89.17% of the variation in SPE and 87.37% of the variation in CO₂ emissions. Among the parameters, layer height has the strongest negative effect on both responses, while printing speed also reduces SPE and CO₂ emissions. In contrast, nozzle temperature and bed temperature have positive effects on both measures. The proposed models provide a quantitative framework for sustainability optimization in additive manufacturing and offer practical guidance for selecting printing parameters to reduce energy consumption and environmental impact.
Additive manufacturing, CO2 emissions, Fused filament fabrication, Specific printing energy, Sustainability
https://doi.org/10.21833/ijaas.2026.03.014
Masmali, M. (2026). A regression analysis of specific printing energy and CO2 emissions in additive manufacturing processes. International Journal of Advanced and Applied Sciences, 13(3), 143–152. https://doi.org/10.21833/ijaas.2026.03.014