Simulation and monitoring in composites manufacture under uncertainty.
| dc.contributor.advisor | Skordos, Alexandros A. | |
| dc.contributor.author | Tifkitsis, Konstantinos I. | |
| dc.date.accessioned | 2023-05-11T08:12:21Z | |
| dc.date.available | 2023-05-11T08:12:21Z | |
| dc.date.issued | 2019-01 | |
| dc.description.abstract | This study focuses on the development of an inversion procedure based on Markov Chain Monte Carlo (MCMC) integrating composites process monitoring with simulation to provide real time probabilistic estimations of process outcomes. The simulation incorporates material and boundary condition uncertainty. Quantification of resin viscosity uncertainty showed a variability of 30% in initial values, introducing variations of an equivalent magnitude in the filling stage of Liquid Composite Moulding (LCM). A surrogate model based on Kriging was developed to enable the use of process models iteratively within a stochastic simulation or optimisation loop. The Kriging model reduces run times by 99% compared to finite element simulation, introducing only an error below 2%. A dielectric sensor appropriate for flow and cure monitoring in the presence carbon reinforcement was developed overcoming limitations of electrical sorting and interference with the electric field. The sensor functionality was demonstrated in both flow and cure LCM trials. Real time flow monitoring was integrated with simulation into an inverse algorithm achieving on line estimation of unknown variables and of the resulting flow field with an error lower than 5%, compared to visual measurements. The inversion was also used in curing, by combining thermal monitoring with simulation to identify the thermal conductivity and heat transfer coefficient probabilistically, leading to estimation of cure duration and final degree of cure with an error below 1%. A stochastic multi-objective optimisation methodology has been developed as a first step towards model based stochastic control of composite manufacturing. The method, which is based on Genetic Algorithms (GA), is capable of identifying process settings that optimise process objectives and their variance. In the case of cure of thick composites, the optimisation identifies cure profiles which achieve 40% reduction in temperature overshoot and process duration compared to standard profiles, whilst achieving increased process robustness through minimisation of the variance. | en_UK |
| dc.description.coursename | PhD in Manufacturing | en_UK |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/19628 | |
| dc.language.iso | en | en_UK |
| dc.rights | © Cranfield University, 2015. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder. | |
| dc.subject | Thermosetting composites | en_UK |
| dc.subject | liquid composite moulding | en_UK |
| dc.subject | inverse problems | en_UK |
| dc.subject | stochastic simulation | en_UK |
| dc.subject | Markov Chain Monte Carlo | en_UK |
| dc.subject | uncertainty qualification | en_UK |
| dc.subject | dielectric monitoring | en_UK |
| dc.subject | surrogate model | en_UK |
| dc.subject | cure | en_UK |
| dc.subject | filling | en_UK |
| dc.title | Simulation and monitoring in composites manufacture under uncertainty. | en_UK |
| dc.type | Thesis | en_UK |