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FUNCTION DRIVEN ASSESSMENT OF MANUFACTURING RISKS IN CONCEPT GENERATION STAGES

Published online by Cambridge University Press:  19 June 2023

Arindam Brahma*
Affiliation:
Chalmers University of Technology, Industrial and Materials Science, Gothenburg, Sweden
Massimo Panarotto
Affiliation:
Chalmers University of Technology, Industrial and Materials Science, Gothenburg, Sweden
Timoleon Kipouros
Affiliation:
University of Cambridge, Department of Engineering, Cambridge, United Kingdom
Ola Isaksson
Affiliation:
Chalmers University of Technology, Industrial and Materials Science, Gothenburg, Sweden
Petter Andersson
Affiliation:
GKN Aerospace Engine Systems, Department of System Analysis & IP, Trollhattan, Sweden
P. John Clarkson
Affiliation:
University of Cambridge, Department of Engineering, Cambridge, United Kingdom
*
Brahma, Arindam, Chalmers University of Technology, Sweden, brahma@chalmers.se

Abstract

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Decisions made in the concept generation phase have a significant effect on the product. While product- related risks typically can be considered in the early stages of design, risks such as supply chain and manufacturing methods are rarely easy to account for in early phases. This is because the currently available methods require mature data, which may not be available during concept generation. In this paper, we propose an approach to address this. First, the product and the non-product (manufacturing and/or supply chain) attributes are modelled using the enhanced function means (EF-M) modelling method. The EF-M method provides the opportunity to model alternative solutions-set for functions. Dependencies are then mapped within the product and the manufacturing models, and also in between them. An automatic combinatorial method of concept generation is employed where each generated instance is a design concept-manufacturing method pair. A risk propagation algorithm is then used to assess the risks of all the generated alternatives.

Information

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), 2023. Published by Cambridge University Press