Porosity is one of the most important properties of textile materials that ensures their comfort and usability. The internal pore structure of the cotton fiber assembly is complex and changeable, surface pore is difficult to explain its pore structure. It is intended to develop a method to predict the pore morphology of cotton fiber assembly. Pore image of the multi-layer fiber assembly is collected by a fiber photography instrument, used the Image Pro Plus 6.0 software to analyze, and obtained the white area indicators of image which can be applied to describe void space of fiber assembly. Using multiple linear regression analysis method, the regression equation of the white area index of image and porosity index of cotton fiber assembly is established. The results indicate that the white area index can largely be explained by three pore index namely the porosity ε, mean length of fiber between the adjacent contacts B and fiber tortuosity coefficient. Appropriate regression equations can be formulated for the pore of white area index which can aid in predicting the pore texture. Comparing the data indicators, it is found that mean length of fiber between the adjacent contacts B and the porosity ε, fiber tortuosity coefficient τ, and air permeability q have good linear correlation.

孔隙率是纺织材料最重要的特性之一，可确保其舒适性和可用性。棉纤维集合体内部孔隙结构复杂多变，表面孔隙难以解释其孔隙结构。旨在开发一种预测棉纤维集合体孔隙形态的方法。用纤维摄影仪采集多层纤维集合体的孔隙图像，利用Image Pro Plus 6.0软件进行分析，得到可用于描述纤维集合体空隙空间的图像白区指标。采用多元线性回归分析方法，建立了棉纤维集合体图像白区指数与孔隙率指数的回归方程。ε，相邻触点B之间的纤维平均长度和纤维弯曲系数。可以为白色区域指数的孔隙制定适当的回归方程，这有助于预测孔隙结构。对比数据指标，发现相邻接触点之间的纤维平均长度B与孔隙率ε、纤维弯曲系数τ、透气度q具有良好的线性相关性。