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Optimising the manufacturing of electrospun nanofibrous structures for textile applications: a machine learning approach

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Optimising the manufacturing of electrospun nanofibrous structures for textile applications: a machine learning approach. / Roldán, Elisa; Reeves, Neil D.; Cooper, Glen et al.
In: Journal of the Textile Institute, 18.03.2025.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Roldán, E, Reeves, ND, Cooper, G & Andrews, K 2025, 'Optimising the manufacturing of electrospun nanofibrous structures for textile applications: a machine learning approach', Journal of the Textile Institute. https://doi.org/10.1080/00405000.2025.2472089

APA

Roldán, E., Reeves, N. D., Cooper, G., & Andrews, K. (2025). Optimising the manufacturing of electrospun nanofibrous structures for textile applications: a machine learning approach. Journal of the Textile Institute. Advance online publication. https://doi.org/10.1080/00405000.2025.2472089

Vancouver

Roldán E, Reeves ND, Cooper G, Andrews K. Optimising the manufacturing of electrospun nanofibrous structures for textile applications: a machine learning approach. Journal of the Textile Institute. 2025 Mar 18. Epub 2025 Mar 18. doi: 10.1080/00405000.2025.2472089

Author

Roldán, Elisa ; Reeves, Neil D. ; Cooper, Glen et al. / Optimising the manufacturing of electrospun nanofibrous structures for textile applications : a machine learning approach. In: Journal of the Textile Institute. 2025.

Bibtex

@article{18c03babf327457c86e1bf053a1bcb2d,
title = "Optimising the manufacturing of electrospun nanofibrous structures for textile applications: a machine learning approach",
abstract = "Electrospun structures, known for their high porosity and surface area, can be tuned by optimising manufacturing parameters. These characteristics make them ideal for waterproof and breathable textiles, skin-like non-woven fabrics, and smart wearable bioelectronic textiles. This research aims to develop a manufacturing optimisation methodology using machine learning models to control fibre diameter and inter-fibre separation for textile applications. Polyvinyl alcohol (PVA) structures were produced with varying concentrations (10, 12, 14, 16 w/v) and different parameters such as flow rate (0.5–5 ml/h), voltage (18–25 kV), needle diameter (15–23 G), distance between needle and collector (5–11 cm), and mandrel revolution (500–3000 rpm). Data from 2560 observations of fibre diameter and inter-fibre separations were used to train 20 machine learning models. C5.0 Decision Trees and Rule-Based Models identified optimal setups, achieving high prediction accuracy for fibre diameter (0.868) and inter-fibre separation (0.861). This research advances the optimisation of electrospinning techniques for textile applications.",
author = "Elisa Rold{\'a}n and Reeves, {Neil D.} and Glen Cooper and Kirstie Andrews",
year = "2025",
month = mar,
day = "18",
doi = "10.1080/00405000.2025.2472089",
language = "English",
journal = "Journal of the Textile Institute",
issn = "0040-5000",
publisher = "Taylor and Francis Ltd.",

}

RIS

TY - JOUR

T1 - Optimising the manufacturing of electrospun nanofibrous structures for textile applications

T2 - a machine learning approach

AU - Roldán, Elisa

AU - Reeves, Neil D.

AU - Cooper, Glen

AU - Andrews, Kirstie

PY - 2025/3/18

Y1 - 2025/3/18

N2 - Electrospun structures, known for their high porosity and surface area, can be tuned by optimising manufacturing parameters. These characteristics make them ideal for waterproof and breathable textiles, skin-like non-woven fabrics, and smart wearable bioelectronic textiles. This research aims to develop a manufacturing optimisation methodology using machine learning models to control fibre diameter and inter-fibre separation for textile applications. Polyvinyl alcohol (PVA) structures were produced with varying concentrations (10, 12, 14, 16 w/v) and different parameters such as flow rate (0.5–5 ml/h), voltage (18–25 kV), needle diameter (15–23 G), distance between needle and collector (5–11 cm), and mandrel revolution (500–3000 rpm). Data from 2560 observations of fibre diameter and inter-fibre separations were used to train 20 machine learning models. C5.0 Decision Trees and Rule-Based Models identified optimal setups, achieving high prediction accuracy for fibre diameter (0.868) and inter-fibre separation (0.861). This research advances the optimisation of electrospinning techniques for textile applications.

AB - Electrospun structures, known for their high porosity and surface area, can be tuned by optimising manufacturing parameters. These characteristics make them ideal for waterproof and breathable textiles, skin-like non-woven fabrics, and smart wearable bioelectronic textiles. This research aims to develop a manufacturing optimisation methodology using machine learning models to control fibre diameter and inter-fibre separation for textile applications. Polyvinyl alcohol (PVA) structures were produced with varying concentrations (10, 12, 14, 16 w/v) and different parameters such as flow rate (0.5–5 ml/h), voltage (18–25 kV), needle diameter (15–23 G), distance between needle and collector (5–11 cm), and mandrel revolution (500–3000 rpm). Data from 2560 observations of fibre diameter and inter-fibre separations were used to train 20 machine learning models. C5.0 Decision Trees and Rule-Based Models identified optimal setups, achieving high prediction accuracy for fibre diameter (0.868) and inter-fibre separation (0.861). This research advances the optimisation of electrospinning techniques for textile applications.

U2 - 10.1080/00405000.2025.2472089

DO - 10.1080/00405000.2025.2472089

M3 - Journal article

JO - Journal of the Textile Institute

JF - Journal of the Textile Institute

SN - 0040-5000

ER -