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A surrogate model based evolutionary game-theoretic approach for optimizing non-isothermal compression RTM processes

Identifieur interne : 000506 ( Main/Exploration ); précédent : 000505; suivant : 000507

A surrogate model based evolutionary game-theoretic approach for optimizing non-isothermal compression RTM processes

Auteurs : A. Gupta [Nouvelle-Zélande] ; P. A. Kelly [Nouvelle-Zélande] ; M. Ehrgott [Nouvelle-Zélande] ; S. Bickerton [Nouvelle-Zélande]

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Abstract

The Compression Resin Transfer Moulding (CRTM) process is a variant of the traditional RTM process and permits significantly faster fill times. However, the design parameters of CRTM processes must be carefully chosen in order to reduce cycle time, capital layout and running costs, while maximizing final part quality. These objectives are principally governed by the filling and curing phases which are strongly coupled in the case of non-isothermal processes. In this work the composites manufacturing cycle is modelled as a static Stackelberg game with two virtual decision makers (DMs) monitoring the filling and curing phases, respectively. The model is implemented through a Bilevel Multiobjective Genetic Algorithm (BMOGA), in conjunction with the Cascade-Correlation Learning Architecture Neural Network (CCA-NN) for function evaluations. The obtained results are efficient with respect to the objectives of both DMs and provide the manufacturer with a diverse set of solutions to choose from.


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Le document en format XML

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<div type="abstract" xml:lang="en">The Compression Resin Transfer Moulding (CRTM) process is a variant of the traditional RTM process and permits significantly faster fill times. However, the design parameters of CRTM processes must be carefully chosen in order to reduce cycle time, capital layout and running costs, while maximizing final part quality. These objectives are principally governed by the filling and curing phases which are strongly coupled in the case of non-isothermal processes. In this work the composites manufacturing cycle is modelled as a static Stackelberg game with two virtual decision makers (DMs) monitoring the filling and curing phases, respectively. The model is implemented through a Bilevel Multiobjective Genetic Algorithm (BMOGA), in conjunction with the Cascade-Correlation Learning Architecture Neural Network (CCA-NN) for function evaluations. The obtained results are efficient with respect to the objectives of both DMs and provide the manufacturer with a diverse set of solutions to choose from.</div>
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