Hostname: page-component-7479d7b7d-wxhwt Total loading time: 0 Render date: 2024-07-11T03:27:25.989Z Has data issue: false hasContentIssue false
  • English
  • Français

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, julianm@chalmers.se

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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

References

Andersson, P., & Isaksson, O. (2008). Manufacturing system to support design concept and reuse of manufacturing experience. In Mitsuishi, M., Ueda, K., & Kimura, F. (Eds.), Manufacturing Systems and Technologies for the New Frontier (pp. 137–140). Springer London. https://doi.org/10.1007/978-1-84800-267-8_28Google Scholar
Blessing, L. T. M., & Chakrabarti, A. (2009). DRM: A Design Reseach Methodology. In DRM, a Design Research Methodology. Springer. https://doi.org/10.1007/978-1-84882-587-1_2CrossRefGoogle Scholar
Souri, El, Gao, M., & Simmonds, J., C. (2019). Integrating manufacturing knowledge with design process to improve quality in the aerospace industry. Procedia CIRP, 84, 374–379. https://doi.org/10.1016/j.procir.2019.04.179CrossRefGoogle Scholar
Jeppsson, P., & Svoboda, A. (1993). Integrated Design and Verification System for Finite Element Modelling. Concurrent Engineering, 1(4), 213–217. https://doi.org/10.1177/1063293X9300100404CrossRefGoogle Scholar
Jiao, J., Simpson, T. W., & Siddique, Z. (2007). Guest Editorial: Product family design and platform-based product development. Journal of Intelligent Manufacturing, 18(1), 1–3. https://doi.org/10.1007/s10845-007-0001-4CrossRefGoogle Scholar
Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the Digital Twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36–52. https://doi.org/10.1016/j.cirpj.2020.02.002CrossRefGoogle Scholar
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474CrossRefGoogle Scholar
Landahl, J., Panarotto, M., Johannesson, H., Isaksson, O., & Lööf, J. (2018). Towards adopting digital twins to support design reuse during platform concept development. Proceedings of NordDesign: Design in the Era of Digitalization, NordDesign 2018.Google Scholar
Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1–10. https://doi.org/10.1016/j.jii.2017.04.005CrossRefGoogle Scholar
Madrid, J., Vallhagen, J., Söderberg, R., & Wärmefjord, K. (2016). Enabling Reuse of Inspection Data to Support Robust Design: A Case in the Aerospace Industry. Procedia CIRP, 43, 41–46. https://doi.org/10.1016/j.procir.2016.02.137CrossRefGoogle Scholar
Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508CrossRefGoogle ScholarPubMed
Schleich, B., Anwer, N., Mathieu, L., & Wartzack, S. (2017). Shaping the digital twin for design and production engineering. CIRP Annals - Manufacturing Technology, 66(1), 141–144. https://doi.org/10.1016/j.cirp.2017.04.040CrossRefGoogle Scholar
Söderberg, R., Wärmefjord, K., Carlson, J. S., & Lindkvist, L. (2017). Toward a Digital Twin for real-time geometry assurance in individualized production. CIRP Annals - Manufacturing Technology, 66(1), 137–140. https://doi.org/10.1016/j.cirp.2017.04.038CrossRefGoogle Scholar
Taguchi, G., & Clausing, D. (1990). Robust quality. Harvard Business Review, 68(1), 65–75.Google Scholar
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. International Journal of Advanced Manufacturing Technology, 94(9-12), 3563–3576. https://doi.org/10.1007/s00170-017-0233-1CrossRefGoogle Scholar
Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, S. C. Y., & Nee, A. Y. C. (2019). Digital twin-driven product design framework. International Journal of Production Research, 57(12), 3935–3953. https://doi.org/10.1080/00207543.2018.1443229CrossRefGoogle Scholar
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital Twin in Industry: State-of-the-Art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/TII.2018.2873186CrossRefGoogle Scholar
Trauer, J., Schweigert-Recksiek, S., Engel, C., Spreitzer, K., & Zimmermann, M. (2020). What Is a Digital Twin? – Definitions and Insights From an Industrial Case Study in Technical Product Development. Proceedings of the Design Society: DESIGN Conference, 1, 757–766. https://doi.org/10.1017/dsd.2020.15CrossRefGoogle Scholar
Trauer, J., Schweigert-Recksiek, S., Okamoto, L. O., Spreitzer, K., Mörtl, M., & Zimmermann, M. (2020). Data-driven engineering definitions and insights from an industrial case study for a new approach in technical product development. Proceedings of the NordDesign 2020 Conference, NordDesign 2020, 1–12. https://doi.org/10.35199/norddesign2020.46CrossRefGoogle Scholar
Ulrich, K. T., & Eppinger, S. D. (2012). Product design and development. McGraw-Hill/Irwin.Google Scholar
Wärmefjord, K., Söderberg, R., Schleich, B., & Wang, H. (2020). Digital twin for variation management: A general framework and identification of industrial challenges related to the implementation. Applied Sciences (Switzerland), 10(10), 3342. https://doi.org/10.3390/APP10103342CrossRefGoogle Scholar