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Reliable Optimisation Control of a Reactive Polymer Composite Moulding Process Using Ant Colony Optimisation and Bootstrap Aggregated Neural Networks

Lookup NU author(s): Dr Jie ZhangORCiD



This paper presents a study on the optimisation control of a reactive polymer composite moulding process using ant colony optimisation and bootstrap aggregated neural networks. In order to overcome the difficulties in developing accurate mechanistic models for reactive polymer composite moulding processes, neural network models are developed from process operation data. Bootstrap aggregated neural networks are used to enhance model prediction accuracy and reliability. Ant colony optimisation is able to cope with optimisation problems with multiple local optima and is able to find the global optimum. Ant colony optimisation is used in this study to find the optimal curing temperature profile. In order to enhance the reliability of the optimisation control policy, model prediction confidence bound offered by bootstrap aggregated neural networks is incorporated in the optimisation objective function so that unreliable predictions are penalised. The proposed method is tested on a simulated reactive polymer composite moulding process.

Publication metadata

Author(s): Mohammed KR, Zhang J

Publication type: Article

Publication status: Published

Journal: Neural Computing & Applications

Year: 2013

Volume: 23

Issue: 7-8

Pages: 1891–1898

Print publication date: 01/12/2013

Date deposited: 05/06/2014

ISSN (print): 0941-0643

ISSN (electronic): 1433-3058

Publisher: Springer


DOI: 10.1007/s00521-012-1273-y


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Funder referenceFunder name
NMP2-SL-2009-228662EU through the project iREMO-intelligent reactive polymer composite moulding