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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Svenja Hauck & Lucas Greif & Nils Benner & Jivka Ovtcharova, 2025. "Advancing Sustainable Additive Manufacturing: Analyzing Parameter Influences and Machine Learning Approaches for CO 2 Prediction," Sustainability, MDPI, vol. 17(9), pages 1-32, April.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:3804-:d:1640717
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    References listed on IDEAS

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    1. Sachin Kumar & T. Gopi & N. Harikeerthana & Munish Kumar Gupta & Vidit Gaur & Grzegorz M. Krolczyk & ChuanSong Wu, 2023. "Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 21-55, January.
    2. Gebler, Malte & Schoot Uiterkamp, Anton J.M. & Visser, Cindy, 2014. "A global sustainability perspective on 3D printing technologies," Energy Policy, Elsevier, vol. 74(C), pages 158-167.
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