The University of Southampton
University of Southampton Institutional Repository

Application of a neural network approach to the electrophoretic deposition of PEEK–alumina composite coatings

Application of a neural network approach to the electrophoretic deposition of PEEK–alumina composite coatings
Application of a neural network approach to the electrophoretic deposition of PEEK–alumina composite coatings
Nano-size Al2O3–polyetheretherketone(PEEK) composite thick films have been prepared on stainless steel substrates from non-aqueous colloidal suspensions by electrophoretic deposition (EPD). The effects on the deposition efficiency of process parameters, such as the deposition time, the difference of potential applied and their interactions were studied using a neural network approach to develop a quantitative understanding of the system. Furthermore the use of the neural network was optimized in the number of epochs, hidden layers and artificial neurons in each hidden layer by a design of experiment (DOE) analysis, demonstrating that these two methods can work together improving the final results of the neural network approach. Afterwards, a MonteCarlo analysis based on a simulation of 100,000 virtual depositions has permitted to deeply investigate the effect of independent variables (e.g. deposition time and difference of potential applied) on the deposition yield (dependent variable).
A. ceramics, A. composites, A. thin films
0025-5408
1494-1501
Corni, Ilaria
f3279082-7093-4a67-b1d7-9ab8bac75b8b
Cannio, Maria
a313f52c-8b49-4fd6-ae09-6bd3b757e0dc
Romagnoli, Marcello
2d9f2d5e-15bb-4539-9dd6-aa7f9e7c4f41
Boccaccini, Aldo R.
847415b9-fdd9-41c0-acb7-3806e53fcabd
Corni, Ilaria
f3279082-7093-4a67-b1d7-9ab8bac75b8b
Cannio, Maria
a313f52c-8b49-4fd6-ae09-6bd3b757e0dc
Romagnoli, Marcello
2d9f2d5e-15bb-4539-9dd6-aa7f9e7c4f41
Boccaccini, Aldo R.
847415b9-fdd9-41c0-acb7-3806e53fcabd

Corni, Ilaria, Cannio, Maria, Romagnoli, Marcello and Boccaccini, Aldo R. (2009) Application of a neural network approach to the electrophoretic deposition of PEEK–alumina composite coatings. Materials Research Bulletin, 44 (7), 1494-1501. (doi:10.1016/j.materresbull.2009.02.011).

Record type: Article

Abstract

Nano-size Al2O3–polyetheretherketone(PEEK) composite thick films have been prepared on stainless steel substrates from non-aqueous colloidal suspensions by electrophoretic deposition (EPD). The effects on the deposition efficiency of process parameters, such as the deposition time, the difference of potential applied and their interactions were studied using a neural network approach to develop a quantitative understanding of the system. Furthermore the use of the neural network was optimized in the number of epochs, hidden layers and artificial neurons in each hidden layer by a design of experiment (DOE) analysis, demonstrating that these two methods can work together improving the final results of the neural network approach. Afterwards, a MonteCarlo analysis based on a simulation of 100,000 virtual depositions has permitted to deeply investigate the effect of independent variables (e.g. deposition time and difference of potential applied) on the deposition yield (dependent variable).

This record has no associated files available for download.

More information

Published date: 1 July 2009
Keywords: A. ceramics, A. composites, A. thin films

Identifiers

Local EPrints ID: 155419
URI: http://eprints.soton.ac.uk/id/eprint/155419
ISSN: 0025-5408
PURE UUID: a9c7b52e-e28a-4c52-9ca0-00991b25ec4e

Catalogue record

Date deposited: 27 May 2010 15:52
Last modified: 14 Mar 2024 01:38

Export record

Altmetrics

Contributors

Author: Ilaria Corni
Author: Maria Cannio
Author: Marcello Romagnoli
Author: Aldo R. Boccaccini

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×