Please use this identifier to cite or link to this item:
https://hdl.handle.net/11499/9288
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kalkancı, Mihriban | - |
dc.contributor.author | Kurumer, G. | - |
dc.contributor.author | Öztürk, H. | - |
dc.contributor.author | Sinecen, M. | - |
dc.contributor.author | Kayacan, Ö. | - |
dc.date.accessioned | 2019-08-16T12:59:29Z | - |
dc.date.available | 2019-08-16T12:59:29Z | - |
dc.date.issued | 2017 | - |
dc.identifier.issn | 1230-3666 | - |
dc.identifier.uri | https://hdl.handle.net/11499/9288 | - |
dc.identifier.uri | https://doi.org/10.5604/01.3001.0010.2859 | - |
dc.description.abstract | The purpose of the present study was to estimate dimensional measure properties of T-shirts made up of single jersey and interlock fabrics through artificial neural networks (ANN). To that end, 72 different types of T-shirts were manufactured under 2 different fabric groups, each was consisting of 2 groups: one with elastane and the other without. Each of these groups were manufactured from six different materials in three different densities through two different knitting techniques of single jersey and interlock. For estimation of dimensional changes in these T-shirts, models including feed-forward, back-propagated, the momentum learning rule and sigmoid transfer function were utilized. As a result of the present study, the ANN system was found to be successful in estimation of pattern measures of garments. The prediction of dimensional properties produced by the neural network model proved to be highly reliable (R2> 0.99). © 2017, Institute of Biopolymers and Chemical Fibres. All rights reserved. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Biopolymers and Chemical Fibres | en_US |
dc.relation.ispartof | Fibres and Textiles in Eastern Europe | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Artificial neural networks | en_US |
dc.subject | Cloth dimensional change | en_US |
dc.subject | Knitted fabric | en_US |
dc.subject | Relaxation | en_US |
dc.subject | Fibers | en_US |
dc.subject | Industrial engineering | en_US |
dc.subject | Textiles | en_US |
dc.subject | Dimensional changes | en_US |
dc.subject | Dimensional properties | en_US |
dc.subject | Garment manufacturing | en_US |
dc.subject | Momentum learning rules | en_US |
dc.subject | Neural network model | en_US |
dc.subject | Sigmoid transfer function | en_US |
dc.subject | Neural networks | en_US |
dc.title | Artificial neural network system for prediction of dimensional properties of cloth in garment manufacturing: Case study on a T-shirt | en_US |
dc.type | Article | en_US |
dc.identifier.volume | 25 | en_US |
dc.identifier.issue | 4 | en_US |
dc.identifier.startpage | 135 | - |
dc.identifier.startpage | 135 | en_US |
dc.identifier.endpage | 140 | en_US |
dc.authorid | 0000-0003-3287-1428 | - |
dc.identifier.doi | 10.5604/01.3001.0010.2859 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopus | 2-s2.0-85029215558 | en_US |
dc.identifier.wos | WOS:000410742100019 | en_US |
dc.identifier.scopusquality | Q2 | - |
dc.owner | Pamukkale University | - |
item.grantfulltext | none | - |
item.openairetype | Article | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 31.02. Textile, Clothing, Shoes and Leather | - |
Appears in Collections: | Buldan Meslek Yüksekokulu Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
CORE Recommender
SCOPUSTM
Citations
11
checked on May 5, 2024
WEB OF SCIENCETM
Citations
10
checked on Aug 1, 2024
Page view(s)
78
checked on Aug 9, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.