Abstract

In this study, the use of a polyester fabric produced from microfibers as a siding material for construction industry was investigated. In this context, an ultraviolet (UV) absorber was applied to the polyester fabric samples with and without dyeing process. These samples were treated with UV absorber at 1–4% concentration and then an accelerated aging test was carried out. The UV absorbance capacity of the samples was investigated before and after the accelerated aging test. Furthermore, the effects of dyeing process on UV absorbance capacity of samples were analyzed. Afterwards, a multilayer perceptron artificial neural network (MLP-ANN) model was proposed and utilized to predict the UV protection properties which are UV protection factor, UV-A and UV-B in polyester microfiber fabric. The MLP-ANN based results demonstrate that the regression (R) values are almost 1 for all UV protection properties. Accordingly, it was seen that the proposed MLP-ANN is correctly modeled and the prediction of UV protection properties is successfully performed.

摘要

在这项研究中，研究了使用由超细纤维制成的聚酯织物作为建筑行业的壁板材料。在这种情况下，将紫外线 (UV) 吸收剂应用于经过和未经染色处理的聚酯织物样品。这些样品用 1-4% 浓度的紫外线吸收剂处理，然后进行加速老化试验。在加速老化试验之前和之后研究了样品的紫外吸收能力。此外，还分析了染色工艺对样品紫外吸收能力的影响。随后，提出了一种多层感知器人工神经网络（MLP-ANN）模型，并利用它来预测涤纶超细纤维织物的紫外线防护性能，即紫外线防护因子、UV-A和UV-B。基于 MLP-ANN 的结果表明回归（R ) 值对于所有紫外线防护性能几乎都为 1。因此，可以看出所提出的 MLP-ANN 被正确建模，并且成功地执行了紫外线防护性能的预测。