Real-time Bayesian Inversion in Resin Transfer Moulding using Neural Surrogates

Causon, Michael

dc.contributor.author	Causon, Michael
dc.contributor.other	Iglesias, Marco
dc.contributor.other	Matveev, Mikhail
dc.contributor.other	Endruweit, Andreas
dc.contributor.other	Tretyakov, Michael
dc.date.accessioned	2024-07-19T07:10:08Z
dc.date.available	2024-07-19T07:10:08Z
dc.date.issued	2024-07-19
dc.identifier.uri	https://rdmc.nottingham.ac.uk/handle/internal/11444
dc.description.abstract	The aim of this study is to rapidly estimate the properties of fibrous reinforcements during the injection phase of Resin Transfer Moulding. There are five data sets associated with this study. The first data set was generated by simulating the resin injection process for 50,000 samples of reinforcement properties. This data was used to train a surrogate model to emulate the injection simulator. The remaining four data sets correspond to the lab experiments included within the paper. These show time series plots for resin pressure at each sensor within the tool, recorded by the data acquisition system.	en_UK
dc.language.iso	en	en_UK
dc.publisher	The University of Nottingham	en_UK
dc.rights	CC-BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.lcsh	Resin transfer molding	en_UK
dc.subject.lcsh	Plastics -- Molding	en_UK
dc.subject.lcsh	Fiber-reinforced plastics	en_UK
dc.title	Real-time Bayesian Inversion in Resin Transfer Moulding using Neural Surrogates	en_UK
dc.type	dataset	en_UK
dc.identifier.doi	http://doi.org/10.17639/nott.7437
dc.subject.free	Resin Transfer Moulding, Moving boundary problems, Neural networks, Surrogate models, Machine Learning	en_UK
dc.subject.jacs	Engineering::Chemical, process & energy engineering	en_UK
dc.subject.lc	T Technology::TP Chemical technology	en_UK
dc.date.collection	Surrogate training data were generated on 14/01/24. Experimental data were collected between 11/12/23 - 04/01/24.	en_UK
uon.division	University of Nottingham, UK Campus::Faculty of Engineering	en_UK
uon.funder.controlled	Engineering & Physical Sciences Research Council	en_UK
uon.datatype	Surrogate training data are text files consisting of the inputs and outputs of the injection simulator. Experimental data recorded via the data acquisition system are in MATLAB data files.	en_UK
uon.grant	EP/P006701/1	en_UK
uon.collectionmethod	The surrogate training data were generated using the "Control volume FEM solver for 2D moving boundary problems" in Matlab (doi:10.5281/zenodo.10914584). The experimental data were collected via a data acquisition system, recording fluid pressure at each sensor within the tool at a rate of 10 per second.	en_UK
uon.institutes-centres	University of Nottingham, UK Campus::Advanced Manufacturing, Institute for	en_UK