Electrospinning predictions using artificial neural networks
- Submitting institution
-
University of Central Lancashire
- Unit of assessment
- 12 - Engineering
- Output identifier
- 12183
- Type
- D - Journal article
- DOI
-
10.1016/j.polymer.2014.12.046
- Title of journal
- Polymer
- Article number
- -
- First page
- 22
- Volume
- 58
- Issue
- -
- ISSN
- 0032-3861
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
K - Jost Institute for Tribotechnology
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Research described this paper was part of an international collaboration between UCLan and Plant and Food Research Ltd (New Zealand). The results have had a significant impact on commercial work in electrospinning plant, making optimisation a considerably less onerous task. It has also been used in electrospinning scoping exercises at UCLan. An open-source version of the predictor described is also being developed for wider distribution. Recognition of this work has led to review of a grant proposal in this area (for the Swiss Data Science Centre (SDSC), a national joint venture between EPFL and ETH Zurich) and journal paper reviews.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
