Pervez Md Nahid, Yeo Wan Sieng, Lin Lina, Xiong Xiaorong, Naddeo Vincenzo, Cai Yingjie
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.
Sci Rep. 2023 Jul 31;13(1):12363. doi: 10.1038/s41598-023-39528-1.
The typical textile dyeing process calls for a wide range of operational parameters, and it has always been difficult to pinpoint which of these qualities is the most important in dyeing performance. Consequently, this research used a combined design of experiments and machine learning prediction models' method to offer a sustainable and beneficial reactive cotton fabric dyeing process. To be more precise, we built a least square support vector regression (LSSVR) model based on Taguchi's statistical orthogonal design (L) to predict exhaustion percentage (E%), fixation rate (F%), and total fixation efficiency (T%) and color strength (K/S) in the reactive cotton dyeing process. The model's prediction accuracy was assessed using many measures, including root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R). Principal component regression (PCR), partial least square regression (PLSR), and fuzzy modelling were some of the other types of regression models used to compare results. Our findings reveal that the LSSVR model greatly outperformed competing models in predicting the E%, F%, T%, and K/S. This is shown by the LSSVR model's much smaller RMSE and MAE values. Overall, it provided the highest possible R values, which reached 0.9819.
典型的纺织品染色过程需要多种操作参数，而且一直很难确定这些参数中哪一个对染色性能最为重要。因此，本研究采用实验设计与机器学习预测模型相结合的方法，以提供一种可持续且有益的活性棉织物染色工艺。更确切地说，我们基于田口统计正交设计（L）构建了最小二乘支持向量回归（LSSVR）模型，以预测活性棉染色过程中的上染率（E%）、固色率（F%）、总固色效率（T%）和色强度（K/S）。使用多种指标评估了该模型的预测准确性，包括均方根误差（RMSE）、平均绝对误差（MAE）和决定系数（R）。主成分回归（PCR）、偏最小二乘回归（PLSR）和模糊建模是用于比较结果的其他几种回归模型类型。我们的研究结果表明，在预测E%、F%、T%和K/S方面，LSSVR模型大大优于竞争模型。这体现在LSSVR模型的RMSE和MAE值要小得多。总体而言，它提供了可能的最高R值，达到了0.9819。
