Publication:
Experimental design for analyzing shade variation in dyeing process

dc.contributor.authorsSennaroglu B.
dc.date.accessioned2022-03-28T15:07:58Z
dc.date.accessioned2026-01-11T13:16:11Z
dc.date.available2022-03-28T15:07:58Z
dc.date.issued2017
dc.description.abstractShade variation occurs when the same textile material is dyed using the same recipe but reflects different color. Shade variation is one of the major problems in textile dyeing industry causing delays, reworking, or even loss of the customer. Even though shade variation cannot be avoided completely, it should be within an acceptable range specified by the customer. In this study, a two-level fractional factorial design for six factors is used to perform an experiment for the dyeing process of knitted cotton fabric. The difference between the reflectance data of the samples from the experiment and the reflectance data of the standard sample is used as the response variable. The statistical analysis of the experimental data is carried out to investigate the factors accounted for shade variation. The objective is to determine influential factors in order to identify opportunities for quality improvement. It is concluded that temperature and liquor ratio are the most influential factors on the shade variation and their two-factor interactions with salt type are also significant. © 2017 IEEE.
dc.identifier.issn21698767
dc.identifier.urihttps://hdl.handle.net/11424/257242
dc.language.isoeng
dc.publisherIEOM Society
dc.relation.ispartofProceedings of the International Conference on Industrial Engineering and Operations Management
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectExperimental design
dc.subjectFractional factorial design
dc.subjectQuality improvement
dc.subjectShade variation
dc.titleExperimental design for analyzing shade variation in dyeing process
dc.typeconferenceObject
dspace.entity.typePublication
oaire.citation.endPage505
oaire.citation.issueJUL
oaire.citation.startPage505
oaire.citation.titleProceedings of the International Conference on Industrial Engineering and Operations Management
oaire.citation.volume2017

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