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Development of the dyeing curve prediction model for dyeing process using multiple linear regression analysis
Journal of the Korean Data & Information Science Society 2018;29:705-15
Published online May 31, 2018
© 2018 Korean Data and Information Science Society.

Suk-gon Yang1 · Hwa-Jung Lee2 · Byeong-Gyu Seo3 · Suk-Bok Kang4

1Advanced Processing Development Center, DYETEC
234Department of Statistics, Yeungnam University
Correspondence to: Professor, Department of Statistics, Yeungnam University, Gyeongbuk, 38541, Korea. E-mail:
Received April 16, 2018; Revised May 14, 2018; Accepted May 17, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
This paper analyses collected data from an actual dyeing process in the field and proposes a prediction model of the rate of dyeing curves based on variables that affect dyeing fixation on polyester fiber. Analysis of variance is used to find varying relationships between the variables and temperate rising velocity and maintain time. The relationships show different results based on color strength. For medium color fabric, the temperature rising velocity is positively correlated with the length and volume of the fabric. For darker color, the rate is found to be negatively correlated with weight of the fabric. A multiple regression model that predicts maintaining time of dyeing process depending on color, weight and length is proposed. A prediction model for the temperature rising velocity based on the same factors is found statistically significant but the coefficient of determination is considered too low for a prediction model.
Keywords : Dyeing curve, maintain time, multiple regression analysis, polyester, rising-temperature velocity, temperature falling time, temperature rising time.