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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A surrogate model based evolutionary game-theoretic approach for optimizing non-isothermal compression RTM processes. / Gupta, Abhishek; Kelly, Piaras A.; Ehrgott, Matthias et al.
In: Composites Science and Technology, Vol. 84, 29.07.2013, p. 92-100.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Gupta, A, Kelly, PA, Ehrgott, M & Bickerton, S 2013, 'A surrogate model based evolutionary game-theoretic approach for optimizing non-isothermal compression RTM processes', Composites Science and Technology, vol. 84, pp. 92-100. https://doi.org/10.1016/j.compscitech.2013.05.012

APA

Gupta, A., Kelly, P. A., Ehrgott, M., & Bickerton, S. (2013). A surrogate model based evolutionary game-theoretic approach for optimizing non-isothermal compression RTM processes. Composites Science and Technology, 84, 92-100. https://doi.org/10.1016/j.compscitech.2013.05.012

Vancouver

Gupta A, Kelly PA, Ehrgott M, Bickerton S. A surrogate model based evolutionary game-theoretic approach for optimizing non-isothermal compression RTM processes. Composites Science and Technology. 2013 Jul 29;84:92-100. Epub 2013 May 28. doi: 10.1016/j.compscitech.2013.05.012

Author

Gupta, Abhishek ; Kelly, Piaras A. ; Ehrgott, Matthias et al. / A surrogate model based evolutionary game-theoretic approach for optimizing non-isothermal compression RTM processes. In: Composites Science and Technology. 2013 ; Vol. 84. pp. 92-100.

Bibtex

@article{1d04f528799f48b0ba4ea9d3a8ddcf3d,
title = "A surrogate model based evolutionary game-theoretic approach for optimizing non-isothermal compression RTM processes",
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.",
keywords = "Optimization, B. Curing, C. Modelling, E. Resin Transfer Moulding (RTM)",
author = "Abhishek Gupta and Kelly, {Piaras A.} and Matthias Ehrgott and Simon Bickerton",
year = "2013",
month = jul,
day = "29",
doi = "10.1016/j.compscitech.2013.05.012",
language = "English",
volume = "84",
pages = "92--100",
journal = "Composites Science and Technology",
issn = "0266-3538",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - A surrogate model based evolutionary game-theoretic approach for optimizing non-isothermal compression RTM processes

AU - Gupta, Abhishek

AU - Kelly, Piaras A.

AU - Ehrgott, Matthias

AU - Bickerton, Simon

PY - 2013/7/29

Y1 - 2013/7/29

N2 - 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.

AB - 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.

KW - Optimization

KW - B. Curing

KW - C. Modelling

KW - E. Resin Transfer Moulding (RTM)

U2 - 10.1016/j.compscitech.2013.05.012

DO - 10.1016/j.compscitech.2013.05.012

M3 - Journal article

VL - 84

SP - 92

EP - 100

JO - Composites Science and Technology

JF - Composites Science and Technology

SN - 0266-3538

ER -