NDLI: Application of artificial neural network (ANN) for prediction of fabrics’ extensibility

Content Provider	Springer Nature Link
Author	Rolich, Tomislav Šajatović, Anica Hursa Pavlinić, Daniela Zavec
Copyright Year	2010
Abstract	In the field of clothing technology, prediction of the fabric properties is very important because the fabric is the basic element of every clothing item. Knowing the fabric properties it is possible to predict fabrics’ behaviour during process of clothing manufacturing (in phase of cutting, sewing and ironing) as well as clothing items’ behaviour during usage. According to the fabrics’ characteristics and model design it is possible to predict appearances of the clothing items and their draping which can be presented with many computer simulations. In this paper extensibility of the fabric which appears during a small forces loading on the fabrics are investigated. Loading of small forces on the fabric appears in each phases of clothing manufacturing processes and during usage of clothing items. Investigations are managed on 50 fabrics which are weaving in twill weave and 100 % wool. The basic characteristics of fabric (density of warp and weft, mass per unit area, thickness) are defined according appropriate standard methods and tensile properties in the warp and weft directions are measured using KES-FB1 measuring system. Using an artificial neural network (ANN) prediction of extensibility properties of the fabrics are done, results are compared with experimental values and deviations are determined. ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase. They can be used to model complex relationships between inputs and outputs or to find patterns in data. Based on the implemented investigations, minimal deviations between experimental and predicted values are obtained and can be concluded that ANN can be used for prediction of the fabrics properties.
Starting Page	917
Ending Page	923
Page Count	7
File Format	PDF
ISSN	12299197
Journal	Fibers and Polymers
Volume Number	11
Issue Number	6
e-ISSN	18750052
Language	English
Publisher	The Korean Fiber Society
Publisher Date	2010-10-06
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Clothing technology Fabric Extensibility Artificial neural network (ANN) Polymer Sciences
Content Type	Text
Resource Type	Article
Subject	Chemistry Chemical Engineering Polymers and Plastics

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Predicting bending rigidity of woven fabrics using artificial neural networks

Predicting properties of single jersey fabrics using regression and artificial neural network models

Artificial neural network approach for predicting colour properties of laser-treated denim fabrics

Prediction of the changes on the CIELab values of fabric after chemical finishing using artificial neural network and linear regression models

Empirical modelling of tensile strength of woven fabrics

An artificial neural network approach to prediction of the colorimetric values of the stripped cotton fabrics

Analysis and tensile-tear properties of abraded denim fabrics depending on pattern relations using statistical and artificial neural network models

Predicting bending rigidity of woven fabrics by neuro-genetic hybrid modeling

Using artificial neural network to predict colour properties of laser-treated 100% cotton fabric

Application of artificial neural network (ANN) for prediction of fabrics’ extensibility

Similar Documents

Predicting bending rigidity of woven fabrics using artificial neural networks

Predicting properties of single jersey fabrics using regression and artificial neural network models

Artificial neural network approach for predicting colour properties of laser-treated denim fabrics

Prediction of the changes on the CIELab values of fabric after chemical finishing using artificial neural network and linear regression models

Empirical modelling of tensile strength of woven fabrics

An artificial neural network approach to prediction of the colorimetric values of the stripped cotton fabrics

Analysis and tensile-tear properties of abraded denim fabrics depending on pattern relations using statistical and artificial neural network models

Predicting bending rigidity of woven fabrics by neuro-genetic hybrid modeling

Using artificial neural network to predict colour properties of laser-treated 100% cotton fabric

Application of artificial neural network (ANN) for prediction of fabrics’ extensibility