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Application of particle swarm optimisation to evaluation of polymer cure kinetics models

Application of particle swarm optimisation to evaluation of polymer cure kinetics models

Tilford, T., Ferenets, M., Morris, J.E., Krumme, A., Pavuluri, S., Rajaguru, P.R. ORCID: 0000-0002-6041-0517, Desmulliez, M.P.Y. and Bailey, C. ORCID: 0000-0002-9438-3879 (2010) Application of particle swarm optimisation to evaluation of polymer cure kinetics models. Journal of Algorithms & Computational Technology, 4 (1). pp. 121-146. ISSN 1748-3018 (doi:https://doi.org/10.1260/1748-3018.4.1.121)

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Abstract

A particle swarm optimisation approach is used to determine the accuracy and experimental relevance of six disparate cure kinetics models. The cure processes of two commercially available thermosetting polymer materials utilised in microelectronics manufacturing applications have been studied using a differential scanning calorimetry system. Numerical models have been fitted to the experimental data using a particle swarm optimisation algorithm which enables the ultimate accuracy of each of the models to be determined. The particle swarm optimisation approach to model fitting proves to be relatively rapid and effective in determining the optimal coefficient set for the cure kinetics models. Results indicate that the singlestep autocatalytic model is able to represent the curing process more accurately than more complex model, with ultimate accuracy likely to be limited by inaccuracies in the processing of the experimental data.

Item Type: Article
Uncontrolled Keywords: particle swarm optimisation algorithm, polymer cure kinetics models, curing process, microelectronics manufacturing, numerical models, evaluation
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Q Science > QA Mathematics
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis > Computational Mechanics & Reliability Group
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis > Computational Science & Engineering Group
School of Computing & Mathematical Sciences > Computer & Computational Science Research Group
School of Computing & Mathematical Sciences > Department of Computer Science
School of Computing & Mathematical Sciences > Department of Computer Systems Technology
School of Computing & Mathematical Sciences > Department of Mathematical Sciences
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Last Modified: 13 Mar 2019 11:32
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/1674

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