Elif GÜLTEKİN, HALİL İBRAHİM ÇELİK, Hatice Nida CİVAN, Eyüp Ali SATIL

SPUNLACE (SU JETİ İLE BAĞLAMA) TEKNOLOJİSİ İLE ÜRETİLEN DOKUSUZ YÜZEYLERİN MORFOLOJİK ÖZELLİKLERİNDEN BAZI PERFORMANS ÖZELLİKLERİNİN YAPAY ZEKA İLE TAHMİNLENMESİ

Dokusuz yüzeylerde lif yerleşimi ve lif dağılım karakteristiği kumaşın fiziksel, mekanik ve geçirgenlik özelliklerini önemli ölçüde etkilemektedir. Literatürdeki çalışmalarda kumaş lif dağılımı ve gözenekliliği ile mekanik ve performans özellikleri arasındaki anlamlı ilişkiler ortaya konmuştur. Bu çalışmada görüntü işleme tekniği kullanılarak geliştirilen algoritma ile dokusuz yüzey kumaş numunelerinden alınan görüntülerden yüzey özniteliklerine ilişkin istatistiksel veriler elde edilmiştir. Elde edilen yüzey öznitelik değerleri hazırlanan yapay sinir ağı modelinde girdi verileri olarak kullanılmıştır. Hava geçirgenliği, makine yönü ve makine tersi yöndeki kopma mukavemeti ve kopma uzaması performans özellikleri ise çıktı verileri olarak kullanılmıştır. Böylece, spunlace (su jeti ile bağlama) teknolojisi ile üretilmiş kumaş numunelerinde doğrudan yüzey görüntülerinden elde edilen doku karakteristiği özellikleri kullanılarak test yapmaksızın hava geçirgenliği, kopma mukavemeti ve kopma uzaması performanslarının tahmin edilmesi hedeflenmiştir. Neticede, deneysel sonuçlar ile yapay sinir ağı tahmin sonuçları arasında hava geçirgenliğinde R2=0,97, kopma mukavemetinde R2=0,90 ve kopma uzamasında R2=0,89 korelasyon katsayısı değerleri elde edilmiştir.

ESTIMATION OF SOME PERFORMANCE PROPERTIES OF NONWOVEN FABRIC PRODUCED WITH SPUNLACE (HYDROENTAGLEMENT) TECHNOLOGY FROM MORPHOLOGICAL CHARACTERISTICS BY USING ARTIFICIAL INTELLIGENCE

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.

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