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Analysis of the relationship between dyeing conditions and K/S using linear regression
Journal of the Korean Data & Information Science Society 2025;36:1-12
Published online January 31, 2025;  https://doi.org/10.7465/jkdi.2025.36.1.1
© 2025 Korean Data and Information Science Society.

Suk-gon Yang1 · Won-June Jang2 · Hwa-Jung Lee3 · Suk-Bok Kang4

1DYETEC Dyeing & Finishing R&D Center
2Anyfive AI & Bigdata Team
34Department of Statistics, Yeungnam University
Correspondence to: This research was conducted as part of the Ministry of Trade, Industry and Energy’s electronic system project[DNA-linked XR Core Components and Service Technology Development].
1 Senior Researcher, Dyeing & Finishing R&D Center, DYETEC, Daegu, 41706, Korea.
2 Deputy Director, AI & Bigdata Team, Anyfive, Seoul, 08390, Korea.
3 Adjunct Professor, Department of Statistics, Yeungnam University, Gyeongbuk, 38541, Korea.
4 Corresponding author: Professor, Department of Statistics, Yeungnam University, Gyeongbuk, 38541, Korea. E-mail: sbkang@ynu.ac.kr
Received November 20, 2024; Revised December 11, 2024; Accepted December 17, 2024.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
This study derives K/S values according to dyeing conditions under controlled experimental conditions based on the material properties of polyester fiber. K/S is a value calculated as the ratio of absorption and scattering of light in the dyed fabric, serving as an indicator for evaluating dyeing quality. Using the measured experimental data, various factors influencing the fixation of disperse dyes onto fiber material samples are identified, and a K/S prediction model is developed. The key factors affecting the dyeing curve are determined through correlation analysis, and the prediction model for K/S values under different dyeing conditions is developed using multiple linear regression analysis. Redundant characteristics among the dyeing conditions are excluded, and the results confirmed that dye amount and processing time significantly affect K/S values. As dye amount increased, the K/S values tended to rise, and processing time was also found to be a critical factor influencing dyeing quality. The K/S prediction model proposed in this study can be effectively utilized to predict dyeing quality and optimize dyeing conditions.
Keywords : Disperse dye, dyeing conditions, K/S, multiple linear regression analysis, polyester fabric