[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121219-en":3,"doc-seo-121219-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121219,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Optimising the manufacturing of electrospun nanofibrous structures for textile applications - a machine learning approach","Electrospun nanofibrous structures feature high porosity and surface area that can be tuned through manufacturing parameters, enabling performance in waterproof/breathable textiles, skin-like non-woven fabrics, and smart wearable bioelectronic textiles. The study develops a machine-learning-based optimisation methodology to control fibre diameter and inter-fibre separation. Polyvinyl alcohol (PVA) samples are produced across multiple concentrations and processing settings. Using 2560 observations, 20 models are trained; C5.0 decision trees and rule-based models yield strong prediction accuracy for fibre diameter (0.868) and inter-fibre separation (0.861).","Please cite the Published Version  \nRoldan Ciudad, Elisa , Reeves, Neil D , Cooper, Glen and Andrews, Kirstie  (2025) Optimising the manufacturing of electrospun nanofibrous structures for textile applications: a machine learning approach. The Journal of The Textile Institute. pp. 1-12. ISSN 0040-5000  \nDOI: [https://doi.org/10.1080/00405000.2025.2472089](https://doi.org/10.1080/00405000.2025.2472089)  \nPublisher: Taylor and Francis  \nVersion: Published Version  \nDownloaded from: [https://e-space.mmu.ac.uk/639118/](https://e-space.mmu.ac.uk/639118/)  \nUsage rights:  Creative Commons: Attribution 4 .0  \nAdditional Information: This is an open access article published in The Journal of The Textile Institute, by Taylor and Francis.  \nData Access Statement: The data supporting this article will be made available on request to the [correspondence author Elisa.Roldan-Ciudad@mmu.ac.uk](correspondence author Elisa.Roldan-Ciudad@mmu.ac.uk).  \nEnquiries:  \nIf you have questions about this document, contact [openresearch@mmu.ac.uk. Please](openresearch@mmu.ac.uk. Please) include the URL of the record in e-space. If you believe that your, or a third party’s rights have been compromised through this document please see our Take Down policy (available from [https://www.mmu.ac.uk/library/using-the-library/policies-and-guidelines](https://www.mmu.ac.uk/library/using-the-library/policies-and-guidelines))  \nThe Journal of The Textile  \nInstitute  \nISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/tjti20)[www.tandfonline.com/journals/tjti20](homepage: www.tandfonline.com/journals/tjti20)  \nOptimising the manufacturing of electrospun nanofibrous structures for textile applications: a machine learning approach  \nElisa Roldán, Neil D. Reeves, Glen Cooper & Kirstie Andrews  \nTo cite this article: Elisa Roldán, Neil D. Reeves, Glen Cooper & Kirstie Andrews (18 Mar 2025): Optimising the manufacturing of electrospun nanofibrous structures for textile applications: a machine learning approach, The Journal of The Textile Institute, DOI:  \n10. 1080/00405000 .2025.2472089  \nTo link to this article: [https://doi.org/10.1080/00405000.2025.2472089](https://doi.org/10.1080/00405000.2025.2472089)  \n© 2025 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group.  \n\n|  Published online: 18 Mar 2025. |  |\n| --- | --- |\n|  | Submit your article to this journal  |\n|  | View related articles  |\n|  View Crossmark data |  |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=tjti20](https://www.tandfonline.com/action/journalInformation?journalCode=tjti20)  \nTHE JOURNAL OF THE TEXTILE INSTITUTE  \n[https://doi.org/10.1080/00405000.2025.2472089](https://doi.org/10.1080/00405000.2025.2472089)  \nRESEARCH ARTICLE     \nOptimising the manufacturing of electrospun nanofibrous structures for textile applications: a machine learning approach  \nElisa Roldna , Neil D. Reevesb, Glen Cooperc and Kirstie Andrewsa  \naDepartment of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, UK; bLancaster Medical School, Faculty of Health and Medicine, Lancaster University, Lancaster, UK; cSchool of Engineering, University of Manchester, Manchester, UK  \nABSTRACT  \nElectrospun 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 bet","cbCaimB6VoF6IueY","https://ap.wps.com/l/cbCaimB6VoF6IueY","pdf",3386263,1,14,"English","en",105,"# Introduction\n## Target textile applications and property requirements\n# Abstract\n## Optimisation methodology using machine learning","[{\"question\":\"What manufacturing outcomes does the research optimise for electrospun textiles?\",\"answer\":\"The work optimises fibre diameter and inter-fibre separation, which define pore size and strongly influence textile performance for specific applications.\"},{\"question\":\"How were the electrospun PVA samples generated for model training?\",\"answer\":\"PVA structures were produced by varying polymer concentration and key processing parameters including flow rate, voltage, needle diameter, needle-to-collector distance, and mandrel revolution.\"},{\"question\":\"Which machine learning approaches achieved the best predictive performance?\",\"answer\":\"C5.0 Decision Trees and Rule-Based Models delivered high prediction accuracy for fibre diameter (0.868) and inter-fibre separation (0.861).\"}]","Optimising the manufacturing of electrospun nanofibrous structures for textile applications - a machine learning approach | PDF",1785734409,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"optimising-the-manufacturing-of-electrospun-nanofibrous-structures-for-textile-applications-a-machine-learning-approach","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/optimising-the-manufacturing-of-electrospun-nanofibrous-structures-for-textile-applications-a-machine-learning-approach/121219/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What manufacturing outcomes does the research optimise for electrospun textiles?","Question",{"text":76,"@type":77},"The work optimises fibre diameter and inter-fibre separation, which define pore size and strongly influence textile performance for specific applications.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the electrospun PVA samples generated for model training?",{"text":81,"@type":77},"PVA structures were produced by varying polymer concentration and key processing parameters including flow rate, voltage, needle diameter, needle-to-collector distance, and mandrel revolution.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning approaches achieved the best predictive performance?",{"text":85,"@type":77},"C5.0 Decision Trees and Rule-Based Models delivered high prediction accuracy for fibre diameter (0.868) and inter-fibre separation (0.861).","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]