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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
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.
- Type
- Artificial Intelligence and Data-Driven Design
- Information
- Creative Commons
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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