Abstract
In order to better judge the fabric style of knitted suit fabrics and improve the production quality of knitted suit fabrics, we use principal component analysis and cluster analysis methods to process fabric samples and evaluation indicators, and use neural network technology to establish The fuzzy neural network model outputs comprehensive evaluation values to judge knitted suit fabrics. The results show that the predicted value of the model output is above 0.6. The style of knitted suit fabric is close to that of traditional woven suit fabric, the flexural stiffness is between 5 and 20 μN• m, the extensibility is between 10% and 20% and the shear stiffness is between 50 N/m. The value of wool and polyester fabric is basically above 0.7, and the style is similar to the woven suit fabric, followed by knitted suit fabrics of cotton and polyester.
Highlights
Suit fabric is a type of fabric used for business apparel, and its fabric style directly affects the quality of clothing
The input, training and test samples of the fuzzy neural network were determined by principal component analysis, cluster analysis, and the model of the fuzzy neural network with 7-14-1 structure was established
According to the characteristics of the output value of the model, when the output value of the model is above 0.6, the fabric style of knitted suit fabric is similar to that of woven suit fabric, the indexes basically meet the requirements of suit fabric and can be used for development and production
Summary
Suit fabric is a type of fabric used for business apparel, and its fabric style directly affects the quality of clothing.Fabric evaluation is divided into subjective evaluation and objective evaluation. Before the establishment of neural network model, the fabric samples are tested to obtain the data of style index, which is the data base for the model.
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