New publication in Sustainability: Advancing Sustainable Additive Manufacturing: Analyzing Parameter Influences and Machine Learning Approaches for CO2 Prediction

Vorschau eines Artikels über nachhaltiges additives Fertigen zur CO₂-Reduktion mittels KI.IMI

The research paper “Advancing Sustainable Additive Manufacturing: Analyzing Parameter Influences and Machine Learning Approaches for CO2 Prediction” was published in the journal Sustainability. Congratulations to the authors Svenja Hauck, Lucas Greif, Nils Benner and Jivka Ovtcharova!

The study investigates how printing parameters influence emissions in the fused deposition modeling (FDM) process - and how artificial intelligence (AI) can predict them. Four parameters (layer height, infill density, perimeter and nozzle temperature) were systematically varied in 81 test prints. The filling density proved to be decisive for material consumption and energy requirements. Of the five AI models tested, XGBoost delivered the best predictions. The results offer valuable approaches for a more sustainable design of 3D printing processes - with benefits for research and industry.

The full publication is available here:
https://doi.org/10.3390/su17093804