Type of Document Master's Thesis Author Gider, Ayse Author's Email Address firstname.lastname@example.org URN etd-10252004-205544 Title An Online Fabric Database to Link Fabric Drape and End-Use Properties Degree Master of Science (M.S.) Department Human Ecology Advisory Committee
Advisor Name Title Yan Chen Committee Chair Jacquelene Robeck Committee Member Jenna T Kuttruff Committee Member Jianhua Chen Committee Member Keywords
- fuzzy linear clustering
- fabric drape
- kawabata fabric evaluation system
- predicting end-use
- cusick drape tester
- fabric database
Date of Defense 2004-05-07 Availability unrestricted AbstractThe main obstacle for adaptation of fabric selection through the Internet is that there is no objective selection method that is suitable for fashion fabrics. The purpose of this research is to develop an objective evaluation method for selecting fabrics through an online fabric database. The relationship between fabric mechanical properties and fabric drape was investigated. One hundred eighty-five commercial fabrics from different manufacturers were tested using the Kawabata fabric evaluation system (KES-FB) and Cusick drape tester. Applying regression analysis, the parameters that were significantly correlated with drape coefficient (DC) were determined.
The test results, fabric structural parameters, and contact information for fabric manufacturers, were included in the database. A web-site with a user interface allowing users to implement various types of searches was published on the Internet. Fuzzy linear clustering technique was used to predict fabric drape property. The accuracy for predicting fabric drape using this technique was 94%. This means the model using fuzzy linear clustering is an efficient method to predict fabric end-use properties.
Additionally, a new method to measure drape coefficient using Photo Shop was developed by this author. Instead of weighing paper rings, shaded drape area was used to calculate the drape coefficient. With the new Photo Shop method, the cost, testing time and human error was reduced while the accuracy of the test result was increased.
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