Comparison of GA-Optimized Viscoelastic Models for the Characterization of Compression Behavior of Warp-Knitted Spacer Fabrics

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Abstract

Nowadays, Warp-Knitted Spacer Fabrics (WKSF) have been widely used for many technical applications. Compressional behavior of WKSF is one of their important properties. Physical modeling is one of the solutions to predict these properties for engineered designing of WKSF. In this study, four common physical models are introduced and compared in order to simulate compressional behavior of polyester WKSF. Genetic Algorithm (GA) was applied to optimize each model parameter. The results showed that the Burger model has the highest adoption with 0.2 percent Mean Absolut Error (MAE). The effect of thickness, outer fabric structure and spacer monofilament density on viscoelastic properties of the samples were also studied.

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