Advancing Sustainable Additive Manufacturing: Analyzing Parameter Influences and Machine Learning Approaches for CO 2 Prediction

Svenja Hauck (), Lucas Greif, Nils Benner and Jivka Ovtcharova
Additional contact information
Svenja Hauck: Institute for Information Management in Engineering, Karlsruhe Institute of Technology, 76133 Karlsruhe, Germany
Lucas Greif: Institute for Information Management in Engineering, Karlsruhe Institute of Technology, 76133 Karlsruhe, Germany
Nils Benner: Institute for Information Management in Engineering, Karlsruhe Institute of Technology, 76133 Karlsruhe, Germany
Jivka Ovtcharova: Institute for Information Management in Engineering, Karlsruhe Institute of Technology, 76133 Karlsruhe, Germany

Sustainability, 2025, vol. 17, issue 9, 1-32

Abstract: The global push for sustainable production, driven by initiatives like the Paris Agreement and the European Green Deal, necessitates reducing CO 2 emissions in industrial processes. Additive manufacturing (AM), with its potential for material efficiency and decentralization, offers promising opportunities for lowering carbon footprints. Due to the significant importance of enhancing the performance of AM via the fine-tuning of printing parameters, this study investigates the dual objectives of understanding parameter influences and leveraging artificial intelligence (AI) to predict CO 2 emissions in fused deposition modeling (FDM) processes. A full-factorial experimental design with 81 test prints was conducted, varying four key parameters—layer height, infill density, perimeters, and nozzle temperature—at three levels (min, mid, and max). The results highlight infill density as the most influential factor, significantly impacting material usage, energy consumption, and overall CO 2 emissions. Five AI algorithms were employed for predictive modeling, with XGBoost demonstrating the highest accuracy in forecasting emissions. By systematically analyzing process interdependencies and providing quantitative insights, this study advances sustainable 3D printing practices. The findings offer practical implications for optimizing AM processes, benefiting both researchers and industrial stakeholders aiming to reduce CO 2 emissions without compromising product integrity.

Keywords: machine learning; additive manufacturing; parameter influences; material and energy consumption; CO 2 prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/9/3804/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/9/3804/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:9:p:3804-:d:1640717

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().