Z. Evrim KANAT, Nilgün ÖZDİL, Arzu MARMARALI

ISLAK HALDEKİ ÖRME KUMAŞLARIN ISIL DİRENÇLERİNİN ÇOKLU REGRESYON ANALİZİ İLE TAHMİNLENMESİ

Kumaşların ısıl özellikleri lif ve iplik tipi, kumaş kalınlığı gibi kumaş konstrüksiyon parametrelerinin yanında kumaşta bulunan nem oranına da bağlıdır. Kumaşın nem oranı arttığında ısıl özellikleri değişmektedir. Isıl direnç kumaş ısıl konforunun temel parametrelerinden birisidir. Kumaş ısıl direnci, kumaşın nem oranı arttığında azalmaktadır. Bu çalışmada pamuk, poliester, modal ve akrilik liflerinden üretilmiş örme kumaşların farklı nem oranlarındaki ısıl direnci iki farklı regresyon analizi ile tahminlenmiştir. Sonuçlar lifin özgül ısısı, lif yoğunluğu, kumaş kalınlığı ve ilmek yoğunluğunun kumaş ısıl direnci için en önemli özellikler olduğunu ve kumaşların farklı nem oranlarında ısıl dirençlerinin regresyon analizi ile tahminlenebileceğini göstermektedir

Anahtar Kelimeler:

Örme kumaş, Isıl direnç, Nem oranı, Regresyon analizi, Tahminleme

PREDICTION OF THERMAL RESISTANCE OF THE KNITTED FABRICS IN WET STATE BY USING MULTIPLE REGRESSION ANALYSIS

Thermal properties of fabric depend on fabric construction parameters such as fiber and yarn type, fabric thickness etc. and also moisture content present. As the moisture content of the fabrics increase thermal properties of the fabrics vary. Thermal resistance is one of the main parameters of thermal comfort of fabrics. Thermal resistance of fabric decreases as the fabric moisture content increases. In this study the thermal resistance of knitted fabrics produced from cotton, polyester, modal and acrylic fibers in different moisture content is predicted from construction parameters by using two different regression analyses. The findings show that specific heat of fiber, fiber density, fabric thickness and loop density are the most important factors for thermal resistance of the fabric and thermal resistance of the fabric in different moisture content can be predicted by using regression analysis successfully

Keywords:

Knitted fabrics, Thermal resistance, Moisture content, Regression analysis, Prediction,

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