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Predictive model for the frictional characteristics of woven fabrics optimized by the genetic algorithm

机译:遗传算法优化的机织织物摩擦特性预测模型

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摘要

Surface friction of fabrics is one of the prominent tactile properties which influence the comfort and application of clothes. In this paper, a new approach is proposed to characterize the surface friction of woven fabrics by presenting a model based on fabric structural parameters. The model coefficients are optimized with the aid of the genetic algorithm, using the experimental friction results obtained from the multi-directional tactile sensing mechanism. The model is developed using the properties of 25 groups of woven fabrics consisting of 5 various weave structures and 5 different weft densities, with similar fibre composition. The statistical analysis of Friction results clarified that the effect of fabric structural parameters such as weave structure and weft density is significant in the confidence range of 95%. The importance of proposing the friction model is that the frictional properties of woven fabrics can be estimated by considering the structural parameters of woven fabrics. This model can be utilized for the forecasting of the friction resistance of various types of woven fabrics without experimental testing procedures.
机译:织物的表面摩擦是影响衣服舒适性和应用的主要触觉特性之一。本文提出了一种新的方法来表征机织织物的表面摩擦力,方法是提出一个基于织物结构参数的模型。利用从多方向触觉感应机构获得的实验摩擦结果,借助遗传算法优化模型系数。该模型是使用25组机织织物的特性开发的,该机织织物由5种不同的编织结构和5种不同的纬密度组成,具有相似的纤维成分。摩擦结果的统计分析表明,织物结构参数(如织物结构和纬纱密度)的影响在95%的置信度范围内显着。提出摩擦模型的重要性在于,可以通过考虑机织织物的结构参数来估计机织织物的摩擦性能。该模型可用于预测各种类型的机织织物的摩擦阻力,而无需进行实验测试程序。

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