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Integration of Value and Sustainability Assessment in Design Space Exploration by Machine Learning: An Aerospace Application
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering. Blekinge Institute of Technology.ORCID iD: 0000-0001-5114-4811
Blekinge Institute of Technology, Faculty of Engineering, Department of Strategic Sustainable Development. Blekinge Institute of Technology.ORCID iD: 0000-0002-7382-1825
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. Blekinge Institute of Technology.ORCID iD: 0000-0002-3311-2530
GKN Aerospace Engine Systems, SWE.
2020 (English)In: Design ScienceArticle in journal (Refereed) Epub ahead of print
Abstract [en]

The use of decision-making models in the early stages of the development of complex products and technologies is a well-established practice in industry. Engineers rely on well-established statistical and mathematical models to explore the feasible design space and make early decisions on future design configurations. At the same time, researchers in both value-driven design and sustainable product development areas have stressed the need to expand the design space exploration by encompassing value and sustainability-related considerations. A portfolio of methods and tools for decision support regarding value and sustainability integration has been proposed in literature, but very few have seen an integration in engineering practices. This paper proposes an approach, developed and tested in collaboration with an aerospace subsystem manufacturer, featuring the integration of value-driven design and sustainable product development models in the established practices for design space exploration. The proposed approach uses early simulation results as input for value and sustainability models, automatically computing value and sustainability criteria as an integral part of the design space exploration. Machine learning is applied to deal with the different levels of granularity and maturity of information among early simulations, value models, and sustainability models, as well as for the creation of reliable surrogate models for multidimensional design analysis. The paper describes the logic and rationale of the proposed approach and its application to the case of a turbine rear structure for commercial aircraft engines. Finally, the paper discusses the challenges of the approach implementation and highlights relevant research directions across the value-driven design, sustainable product development, and machine learning research fields.

Place, publisher, year, edition, pages
Cambridge University Press, 2020.
Keywords [en]
decision-making, value-driven design, sustainable product development, design space exploration, machine learning, surrogate models
National Category
Mechanical Engineering Computer Sciences
Identifiers
URN: urn:nbn:se:bth-17851DOI: 10.1017/dsj.2019.29OAI: oai:DiVA.org:bth-17851DiVA, id: diva2:1307067
Funder
Knowledge Foundation
Note

open access

Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2020-02-06Bibliographically approved
In thesis
1. Tree Models for Design Space Exploration in Aerospace Engineering
Open this publication in new window or tab >>Tree Models for Design Space Exploration in Aerospace Engineering
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

A crucial issue in the design of aircraft components is the evaluation of a larger number of potential design alternatives. This evaluation involves too expensive procedures, consequently, it slows down the search for optimal design samples. As a result, scarce or small number of design samples with high dimensional parameter space and high non-linearity pose issues in learning of surrogate models. Furthermore, surrogate models have more issues in handling qualitative data (discrete) than in handling quantitative data (continuous). These issues bring the need for investigations of methods of surrogate modelling for the most effective use of available data. 

 The thesis goal is to support engineers in the early design phase of development of new aircraft engines, specifically, a component of the engine known as Turbine Rear Structure (TRS). For this, tree-based approaches are explored for surrogate modelling for the purpose of exploration of larger search spaces and for speeding up the evaluations of design alternatives. First, we have investigated the performance of tree models on the design concepts of TRS. Second, we have presented an approach to explore design space using tree models, Random Forests. This approach includes hyperparameter tuning, extraction of parameters importance and if-then rules from surrogate models for a better understanding of the design problem. With this presented approach, we have shown that the performance of tree models improved by hyperparameter tuning when using design concepts data of TRS. Third, we performed sensitivity analysis to study the thermal variations on TRS and hence support robust design using tree models. Furthermore, the performance of tree models has been evaluated on mathematical linear and non-linear functions. The results of this study have shown that tree models fit well on non-linear functions. Last, we have shown how tree models support integration of value and sustainability parameters data (quantitative and qualitative data) together with TRS design concepts data in order to assess these parameters impact on the product life cycle in the early design phase.

 

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2019. p. 149
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 8
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17852 (URN)978-91-7295-377-2 (ISBN)
Presentation
2019-06-03, 13:00 (English)
Opponent
Supervisors
Available from: 2019-04-26 Created: 2019-04-25 Last updated: 2019-06-11Bibliographically approved

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Bertoni, AlessandroHallstedt, SophieDasari, Siva Krishna

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