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Optimisation of aero-manufacturing characteristics of aircraft ribs

Published online by Cambridge University Press:  08 February 2022

T. Kim Open the ORCID record for T. Kim [Opens in a new window] ,
T. Kipouros ,
A. Brintrup ,
J. Farnfield  and
D. Di Pasquale
T. Kim*
Affiliation:
Institute for Manufacturing, University of Cambridge, Cambridge, UK
T. Kipouros
Affiliation:
Department of Engineering, University of Cambridge, Cambridge, UK
A. Brintrup
Affiliation:
Institute for Manufacturing, University of Cambridge, Cambridge, UK
J. Farnfield
Affiliation:
GKN Aerospace Services, Filton, UK
D. Di Pasquale
Affiliation:
School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, UK
*
E-mail: teddy9835@gmail.com
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Abstract

The main purpose of this study was to combine the currently separate objectives of aerodynamic performance and manufacturing efficiency, then find an optimal point of operation for both objectives. An additional goal of the study was to explore the effects of changes in design features, the position of the spars, and analyse how the changes influenced the optimal operating conditions. A machine-learning approach was taken to combine and model the gathered aero-manufacturing data, and a multi-objective optimisation approach utilising genetic algorithms was implemented to find the trade-off relationship between optimal target objectives (mission performance and manufacturability). The main achievements and findings of the study were: The study was a success in building a machine-learning model for the combined aero-manufacturing data utilising software library XGBoost; multi-objective optimisation, which did not include spar positions as a variable found the trade-off region between high manufacturability and high mission performance, with choices that offered reasonably high values of both; there was no clearly identified correlation between a small change in spar position and the target objectives; multi-objective optimisation with spar positions resulted in a trade-off relationship between target objectives, which was different from the trade-off relationship found in optimisation without spar positions; multi-objective optimisation with spar positions also offered more flexibility in the choice of manufacturing processes available for a given design; and the range of bump amplitudes for solutions found by multi-objective-optimisation with spar positions was lower and more focused than those found by optimisation without spar positions.

Keywords

OptimisationManufacturingMachine Learning
Type
Research Article
Information
The Aeronautical Journal , Volume 126 , Issue 1299 , May 2022 , pp. 866 - 888
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Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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