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Improving sustainability of additive manufacturing processes based on digital twins – a case study

Published online by Cambridge University Press:  16 May 2024

Jessica Kos ,
Philipp Schröder ,
Jakob Trauer ,
Felix Endress ,
Markus Mörtl  and
Markus Zimmermann
Jessica Kos
Affiliation:
Technical University of Munich, TUM School of Engineering and Design, Laboratory for Product Development and Lightweight Design, Germany
Philipp Schröder*
Affiliation:
Technical University of Munich, TUM School of Engineering and Design, Laboratory for Product Development and Lightweight Design, Germany
Jakob Trauer
Affiliation:
:em engineering methods AG, Germany
Felix Endress
Affiliation:
Technical University of Munich, TUM School of Engineering and Design, Laboratory for Product Development and Lightweight Design, Germany
Markus Mörtl
Affiliation:
Technical University of Munich, TUM School of Engineering and Design, Laboratory for Product Development and Lightweight Design, Germany
Markus Zimmermann
Affiliation:
Technical University of Munich, TUM School of Engineering and Design, Laboratory for Product Development and Lightweight Design, Germany
*
philipp.schroeder@tum.de

Abstract

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Additive manufacturing (AM) became a key technology in the development of innovative products. Advancements have been made to improve economic feasibility. However, ecological sustainability is still an open issue of AM. To improve sustainability, it is crucial to track, visualize, and evaluate emissions along the lifecycle. This paper presents a novel Digital Twin based approach enabling prediction of the product carbon footprint (PCF) and prescriptive measures to improve sustainability. By improving part and process design, a significant PCF reduction was achieved.

Keywords

digital twinadditive manufacturingsustainabilitysustainable designproduct carbon footprint

Information

Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2089 - 2098
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

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