dc.contributor.author |
Gupta, A |
en |
dc.contributor.author |
Ong, YS |
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dc.contributor.author |
Kelly, Piaras |
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dc.contributor.author |
Goh, CK |
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dc.coverage.spatial |
Vancouver, Canada |
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dc.date.accessioned |
2018-10-02T02:14:53Z |
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dc.date.issued |
2016-01-01 |
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dc.identifier.uri |
http://hdl.handle.net/2292/38153 |
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dc.description.abstract |
Compression Resin Transfer Moulding is a popular method for high volume production of superior quality fibre-reinforced polymer composite parts. However, the process involves a large number of design variables that 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 two separate phases of the manufacturing cycle, namely the resin filling and curing phases. It turns out that independently optimizing either phase (which is the general practice) may often lead to conditions that significantly restrict or even adversely affect the progress of the other. In light of this fact, a novel approach of modelling the entire composites manufacturing problem as bi-level program, one that assimilates both phases, has been adopted in this paper. In particular, an efficient multi-objective bi-level evolutionary algorithm is designed to effectively deal with the computationally expensive simulation-based optimization problem. The unique feature of the algorithm is that it incorporates a Pareto Rank Learning scheme, together with surrogate assistance for the upper level problem, in order to eliminate several expensive but redundant objective function evaluations. The optimization process is therefore considerably accelerated, assisting manufacturers in making improved decisions for this complex engineering design problem. |
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dc.publisher |
IEEE |
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dc.relation.ispartof |
IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI) |
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dc.relation.ispartofseries |
2016 IEEE Congress on Evolutionary Computation (CEC) |
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dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
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dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
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dc.subject |
Science & Technology |
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dc.subject |
Technology |
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dc.subject |
Computer Science, Artificial Intelligence |
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dc.subject |
Computer Science |
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dc.subject |
Multi-Objective Bi-Level Optimization |
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dc.subject |
Evolutionary Algorithm |
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dc.subject |
Pareto Rank Learning |
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dc.subject |
Surrogate Modelling |
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dc.subject |
Composites Manufacturing |
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dc.subject |
ALGORITHM |
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dc.subject |
CURE |
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dc.title |
Pareto Rank Learning for Multi-Objective Bi-Level Optimization: A Study in Composites Manufacturing |
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dc.type |
Conference Item |
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dc.identifier.doi |
10.1109/CEC.2016.7744025 |
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pubs.begin-page |
1940 |
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dc.rights.holder |
Copyright: The author |
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pubs.end-page |
1947 |
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pubs.finish-date |
2016-07-29 |
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pubs.publication-status |
Published |
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pubs.start-date |
2016-07-24 |
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dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
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pubs.subtype |
Proceedings |
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pubs.elements-id |
606399 |
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pubs.org-id |
Engineering |
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pubs.org-id |
Engineering Science |
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pubs.record-created-at-source-date |
2019-02-08 |
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