Sustainable fashion: Design of the experiment assisted machine learning for the environmental-friendly resin finishing of cotton fabric
Md Nahid Pervez,
Wan Sieng Yeo,
Faizan Shafiq,
Muhammad Munib Jilani,
Zahid Sarwar,
Mumtahina Riza,
Lina Lin,
Xiaorong Xiong,
Vincenzo Naddeo,
Yingjie Cai
Affiliations
Md Nahid Pervez
Hubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-based Textile Materials, Wuhan Textile University, Wuhan 430200, China; School of Computing, Huanggang Normal University, Huanggang 438000, China; Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Fisciano 84084, Italy
Wan Sieng Yeo
Department of Chemical and Energy Engineering, Faculty of Engineering and Science, Curtin University Malaysia, CDT 250, 98009 Miri, Sarawak, Malaysia
Faizan Shafiq
Hubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-based Textile Materials, Wuhan Textile University, Wuhan 430200, China
Muhammad Munib Jilani
Department of Textile Processing, National Textile University, Faisalabad, Punjab 37610, Pakistan
Zahid Sarwar
School of Engineering and Technology, National Textile University, Faisalabad, Punjab 37610, Pakistan
Mumtahina Riza
Department of Applied Ecology, North Carolina State University, Campus Box 7617 Raleigh, NC 27695-7617, USA
Lina Lin
Hubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-based Textile Materials, Wuhan Textile University, Wuhan 430200, China; Corresponding author. .
Xiaorong Xiong
School of Computing, Huanggang Normal University, Huanggang 438000, China; Corresponding author. .
Vincenzo Naddeo
Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Fisciano 84084, Italy; Corresponding author. .
Yingjie Cai
Hubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-based Textile Materials, Wuhan Textile University, Wuhan 430200, China
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® approved a formaldehyde-free Knittex RCT® 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.
