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EXPLORING THE POTENTIAL OF DIGITAL TWIN-DRIVEN DESIGN OF AERO-ENGINE STRUCTURES

Published online by Cambridge University Press:  27 July 2021

Julian Martinsson ,
Massimo Panarotto ,
Michael Kokkolaras  and
Ola Isaksson
Julian Martinsson*
Affiliation:
Chalmers tekniska högskola AB;
Massimo Panarotto
Affiliation:
Chalmers tekniska högskola AB;
Michael Kokkolaras
Affiliation:
McGill University
Ola Isaksson
Affiliation:
Chalmers tekniska högskola AB;
*
Martinsson, Julian, Chalmers tekniska högskola AB, Sweden, [email protected]

Abstract

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As the diversity of customer needs increases within the aerospace industry, so does the need for improved design practices to reduce quality issues downstream. When designing new products, design engineers struggle with applying tolerances to features, which often leads to expensive late design iterations. To mitigate this, one aerospace company is looking to reuse tolerance deviation data yielded during manufacturing in design. In the long term these data could provide the basis for a Digital Twin that can be used for improved product development. This article explores how data from production are used today, what issues prevents such data from being exploited in the design phase, and how they potentially could be used for design purposes in the future. To understand the current situation and identify the untapped potential of production data in design, an interview study was conducted in conjunction with a literature review. In this paper the current situation and primary barriers are presented and a possible path for further research and development is suggested.

Keywords

Digital TwinData-Driven EngineeringDesign methodologyIndustry 4.0Design process
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 1521 - 1528
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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