Sarkar Joy, Prottoy Zawad Hasan, Bari Md Tanimul, Al Faruque Md Abdullah
Department of Textile Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh.
Department of Fabric Engineering, Bangladesh University of Textiles, Dhaka 1208, Bangladesh.
Heliyon. 2021 Sep 15;7(9):e08000. doi: 10.1016/j.heliyon.2021.e08000. eCollection 2021 Sep.
Nowadays, the polyurethane and its derivatives are highly applied as a surface modification material onto the textile substrates in different forms to enhance the functional properties of the textile materials. The primary purpose of this study is to develop prediction models to model the absorption property of the textile substrate using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods. In this study, polyurethane (PU) along with acrylic binder was applied on the dyed polyester knitted fabric to develop and validate the prediction models. Through the morphological study, it was evident that the solution prepared with the polyurethane and the acrylic binder was effectively coated onto the fabric surface. The ANFIS model was constructed by considering binder (ml) and PU (%) as input parameters, whereas absorbency (%) was the only output parameter. On the other hand, the system was trained with 70% data for constructing the ANN model whereas testing and validation were done with 15% data, respectively. To train the network, feed-forward backpropagation with Levenberg-Marquardt learning algorithm was used. The coefficient of determination (R) was found to be 0.98 and 0.93 for ANFIS and ANN model, respectively. Both prediction models exhibited an excellent mean absolute error percentage (0.76% for the ANFIS model and 1.18% for the ANN model). Furthermore, an outstanding root-mean-square error (RMSE) of 0.61% and 1.28% for ANFIS and ANN models was observed. These results suggested an excellent performance of the developed models to predict the absorption property of the polyurethane and acrylic binder treated fabric. Besides, these models can be taken as a basis to develop prediction models for specific types of functional applications of the textile materials to eliminate heaps of trial and error efforts of the textile industries, which eventually be helpful in the scalable production of functional textiles.
如今，聚氨酯及其衍生物作为一种表面改性材料，以不同形式被广泛应用于纺织基材上，以增强纺织材料的功能特性。本研究的主要目的是使用自适应神经模糊推理系统（ANFIS）和人工神经网络（ANN）方法开发预测模型，以模拟纺织基材的吸收性能。在本研究中，将聚氨酯（PU）与丙烯酸粘合剂一起应用于染色聚酯针织物上，以开发和验证预测模型。通过形态学研究发现，由聚氨酯和丙烯酸粘合剂制备的溶液有效地涂覆在了织物表面。ANFIS模型是将粘合剂（毫升）和PU（%）作为输入参数构建的，而吸光度（%）是唯一的输出参数。另一方面，使用70%的数据训练系统以构建ANN模型，而分别用15%的数据进行测试和验证。为了训练网络，使用了带有Levenberg-Marquardt学习算法的前馈反向传播。发现ANFIS模型和ANN模型的决定系数（R）分别为0.98和0.93。两个预测模型均表现出优异的平均绝对误差百分比（ANFIS模型为0.76%，ANN模型为1.18%）。此外，观察到ANFIS模型和ANN模型的均方根误差（RMSE）分别出色地为0.61%和1.28%。这些结果表明所开发的模型在预测聚氨酯和丙烯酸粘合剂处理过的织物的吸收性能方面表现出色。此外，这些模型可作为开发纺织材料特定功能应用预测模型的基础，以消除纺织行业大量的反复试验工作，这最终将有助于功能性纺织品的规模化生产。
