超细纤维绒面革热湿舒适性能的预测与分析

Prediction and Analysis of Thermal and Wet Comfort Performance of Microfiber Suede Leather

通过正交试验,采用统计回归和神经网络两种方法建立了以水性聚氨酯(WPU)含量、固化温度和超纤革的减量程度为变量的数学模型,对超细纤维绒面革的热湿舒适性能进行预测。对模型进行正态性检验及方差分析,并对影响因素进行分析比较,评估模型的预测能力。结果表明,WPU含量对透湿性有显著影响,减量程度对透气性影响最大。透气性和透湿性在统计回归预测模型中的拟合系数分别为0.871和0.865,而在神经网络预测模型中的拟合系数分别为0.997和0.989。综合来看,神经网络的预测效果优于统计回归模型。

A mathematical model with waterborne polyurethane(WPU)content,temperature during the curing process and the reduction degree of microfiber leather as variables was developed to predict the thermal and humidity comfort performance of microfiber suede leather by performing orthogonal experimentation,using both statistical regression and neural network methods.The model was subjected to normality testing and analysis of variance,and the influencing factors were analyzed and compared to assess the predictive ability of the model.The results showed that the WPU content had a significant effect on moisture permeability,and the degree of reduction had the greatest effect on breathability.The fitting coefficients of breathability and moisture permeability in the statistical regression prediction model were 0.871 and 0.865,respectively,while these coefficients in the neural network prediction model were 0.997 and 0.989,respectively.Overall,the neural network model performed better than the statistical regression model in predicting the thermal and wet comfort of microfiber suede leather.