4.5 Article

Sustainable fashion: Design of the experiment assisted machine learning for the environmental-friendly resin finishing of cotton fabric

期刊

HELIYON
卷 9, 期 1, 页码 -

出版社

CELL PRESS
DOI: 10.1016/j.heliyon.2023.e12883

关键词

Resin finishing; Sustainability; Free formaldehyde; Taguchi; Machine learning

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Given the carcinogenic properties of formaldehyde-based chemicals, an alternative method for resin-finishing cotton textiles is needed urgently. A sustainable resin-finishing process for cotton fabric was introduced using a formaldehyde-free Knittex RCT resin approved by Bluesign (R), and the process parameters were optimized using the Taguchi L27 method. XRD analysis confirmed the crosslinking formation between resin and cotton fabric without altering the cellulose crystallization phase.
Given the carcinogenic properties of formaldehyde-based chemicals, an alternative method for resin-finishing cotton textiles is urgently needed. Therefore, the primary objective of this study is to introduce a sustainable resin-finishing process for cotton fabric via an industrial procedure. For this purpose, Bluesign (R) approved a formaldehyde-free Knittex RCT (R) resin was used, and the process parameters were designed and optimized according to the Taguchi L27 method. XRD analysis confirmed the crosslinking formation between resin and neighboring molecules of cotton fabric, as no change in the cellulose crystallization phase. Several machine learning models were built in a sequence to predict the crease recovery angle (CRA), tearing strength (TE) and whiteness index (WI). Assessment of modelling was evaluated through the use of various metrics such as root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). Results were compared to those from other regression models, such as principal component regression (PCR), partial least squares regression (PLSR), and fuzzy modelling. Based on the results of our research, the LSSVR model predicted the CRA, TE, and WI with substantially more accuracy than other models, as shown by the fact that its RMSE and MAE values were significantly lower. In addition, it offered the greatest possible R2 values, reaching up to 0.9627.

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