Analysis and Prediction of Air Permeability of 100% Cotton Single Jersey Fabric Using Machine Learning Approaches

Sharmin Akter, Sohel Ahammed, Md. Mashiur Rahman Khan


The air permeability property has great significance on the fabric comfort related property. In this study Artificial Neural Network (ANN), Random Forest and Additive Regression Classification Models have been applied for the prediction of the air permeability of single jersey knitted fabrics made of 100% cotton fiber. For this aim, 100 different single jersey knitted fabrics were used and there basic properties such as yarn linear density, tightness factor, fabric loop length, fabric thickness, stitch density, fabric unit weight, and air permeability properties were evaluated. ANN, Random Forest, and Additive Regression Classification Models were developed to predict air permeability properties of single jersey fabrics. It was found that all the models give outputs closer to the experimental results. However, ANN estimation success was found higher than other models.

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