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MODEL-BASED DESIGN OF AM COMPONENTS TO ENABLE DECENTRALIZED DIGITAL MANUFACTURING SYSTEMS

Published online by Cambridge University Press:  27 July 2021

Olivia Borgue*
Affiliation:
Chalmers University of Technology;
John Stavridis
Affiliation:
Prima Industries SpA; Politecnico di Torino;
Tomas Vannucci
Affiliation:
RISE Research Institutes of Sweden;
Panagiotis Stavropoulos
Affiliation:
University of Patras
Harry Bikas
Affiliation:
University of Patras
Rosa Di Falco
Affiliation:
Prima Industries SpA; Politecnico di Torino;
Lars Nyborg
Affiliation:
Chalmers University of Technology;
*
Borgue, Olivia, Chalmers University of Technology, Industrial and Material Sciences, Sweden, borgue@chalmers.se

Abstract

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Additive manufacturing (AM) is a versatile technology that could add flexibility in manufacturing processes, whether implemented alone or along other technologies. This technology enables on-demand production and decentralized production networks, as production facilities can be located around the world to manufacture products closer to the final consumer (decentralized manufacturing). However, the wide adoption of additive manufacturing technologies is hindered by the lack of experience on its implementation, the lack of repeatability among different manufacturers and a lack of integrated production systems. The later, hinders the traceability and quality assurance of printed components and limits the understanding and data generation of the AM processes and parameters. In this article, a design strategy is proposed to integrate the different phases of the development process into a model-based design platform for decentralized manufacturing. This platform is aimed at facilitating data traceability and product repeatability among different AM machines. The strategy is illustrated with a case study where a car steering knuckle is manufactured in three different facilities in Sweden and Italy.

Type
Article
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), 2021. Published by Cambridge University Press

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