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New Approaches to Establishing Translation Equations for the Total Hand Value of Fabric

机译：建立织物总手价值平移方程的新方法

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The aim of this study is to take new approaches using a one-step transformation process to establish translation equations for total hand evaluations of fabrics by employing a stepwise regression method and an artificial neural network. The key mechanical properties selected from sixteen fabric mechanical properties based on a KES system, using the stepwise regression selection method, are the parameters. The translation equations are developed directly with parameters without a primary hand value transformation process. In this study, 114 polyester/cotton blended woven fabrics are selected for investigation. From the results, four mechanical properties LC, 2HG, B, and WT are the parameters for developing the translation equations. The correlation coefficients of the translation equations developed from the stepwise regression and artificial neural network methods are 0.925 and 0.955, respectively. Both translation equations have high correlation coefficients between the calculated and practical values. The approaches are identified effectively to develop translation equations for new fabrics in the textile industry.
机译：这项研究的目的是采用一种新的方法，通过一步一步的转换过程，通过逐步回归方法和人工神经网络，为织物的总手性评估建立转化方程。使用逐步回归选择方法从基于KES系统的16种织物机械性能中选择的关键机械性能是参数。转换方程式直接使用参数进行开发，而无需进行初级手值转换过程。在这项研究中，选择了114种聚酯/棉混纺机织织物进行调查。从结果来看，四个机械性能LC，2HG，B和WT是建立平移方程的参数。由逐步回归法和人工神经网络法得到的平移方程的相关系数分别为0.925和0.955。这两个平移方程在计算值和实际值之间都有很高的相关系数。有效地确定了这些方法，以开发纺织工业中新织物的平移方程。

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