DEVELOPING CNN-AUGMENTED MODELS TO PREDICT CIELAB OUTCOMES POSTBLEACHING OF DENIM GARMENTS

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dc.contributor.authorKalkan, İbrahim Erdem
dc.contributor.authorÇalişkan, Ebru
dc.contributor.authorŞahin, Cenk
dc.contributor.authorBalci, Onur
dc.contributor.authorKuvvetli, Yusuf
dc.contributor.organizationTechnická univerzita v Liberci
dc.date.accessioned2025-04-07T07:59:16Z
dc.date.available2025-04-07T07:59:16Z
dc.description.abstractDenim garment production demands efficient design processes to minimize waste, costs, and production delays. Bleaching, among other finishing processes, holds paramount importance due to its numerous variables and substantial impact on product value. Artificial neural networks have great potential to achieve superior performance in anticipating various process outcomes. Their parameterized structure effectively captures non-linear relationships between input features. This study aims to effectively predict fabric outcomes by developing an artificial neural network (ANN) model supported by convolutional neural networks (CNN) to provide additional features derived from raw and semi-processed fabric images. The study represents a comparison of CNN powered models with a common predictive ANN as base model. Competing models incorporate various process variables and fabric properties, such as dying number and elasticity to predict changes in denim CIELab properties after bleaching. The process features of the model are the number of bleaching cycles, total process time, and concentration of sodium hypochlorite (representing the total amount of chemical used). The mean absolute percentage error is used as the performance measure between predictions and desired outputs. This research plays a significant role in enhancing agility in denim production by providing businesses with more efficient approaches to digitized denim bleaching and Research and Development processes in the textile industry.cs
dc.formattext
dc.format.extent5 stran
dc.identifier.doi10.15240/tul/008/2025-1-010
dc.identifier.issn1335-0617
dc.identifier.urihttps://dspace.tul.cz/handle/15240/176803
dc.language.isocscs
dc.publisherTechnical University of Liberec
dc.publisher.abbreviationTUL
dc.relation.ispartofFibres and Textiles
dc.subjectDenimcs
dc.subjectBleachingcs
dc.subjectEffectcs
dc.subjectArtificial neural networkscs
dc.titleDEVELOPING CNN-AUGMENTED MODELS TO PREDICT CIELAB OUTCOMES POSTBLEACHING OF DENIM GARMENTSen
dc.typeArticleen
local.accessopen access
local.citation.epage57
local.citation.spage53
local.facultyFaculty of Textile Engineeringen
local.fulltextyesen
local.relation.issue1
local.relation.volume32
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