Spunlace (Su Jeti ile Bağlama) Teknolojisi ile Üretilen Dokusuz Yüzeylerin Morfolojik Özelliklerinden Bazı Performans Özelliklerinin Yapay Zeka ile Tahminlenmesi

Fiber placement and fiber distribution characteristics of nonwoven surfaces significantly affect the physical, mechanical and permeability properties of the fabric. In the studies from the literature, significant relationships between fiber distribution and porosity with mechanical performance properties have been revealed. In this study, an algorithm developed using image processing techniques for statistical data related to texture features were obtained images from nonwoven surface fabric samples. The texture features obtained were used as input data in the artificial neural network model. Air permeability, machine direction breaking strength, cross direction breaking strenth, and breaking elongation performance characteristics were used as output data. Thus, it is aimed to estimate the air permeability, breaking strength and breaking elongation performances of the fabric samples produced with spunlace (hydroentaglement bonding) technology without testing by using the texture characteristic features obtained directly from the surface images. As a result, the correlation coefficient values of R2 = 0,97 in air permeability, R2 = 0,90 in breaking strength and R2 = 0,89 in breaking elongation were obtained between experimental results and artificial neural network prediction results.