Modeling and Prediction of the Air Permeability of Fabrics Based on the Support Vector Machine

Abstract

Abstract Air permeability is an important index of textiles and has a significant effect on the quality of the fabric. Thus, the air permeability measured by the air permeability tester plays an important role in the textile industry. However, the accuracy of the tester is determined by prediction model precision. A new prediction model for air permeability based on the support vector machine (SVM) was presented in the paper, which can improve the measurement precision and stability of the tester. Three groups of measured open data were used to verify the validity of the model. For each group, 27 samples were used as training data to create the support vector machine regression (SVR) model. The mean square error (MSE) and the correlation coefficient (R) were introduced to evaluate the model. For the selected 3#, 4#, and 6# nozzles, the R values of the SVM regression model (Model 1) were 0.9988, 0.9992, and 0.9995, respectively, whereas for the traditional power function (Model 2), those were 0.9938, 0.9951, and 0.9898, respectively, indicating Model 1 has better correlation and agreement. In addition, the MSE values of Model 1 were 2.3978, 1.5186, and 0.9314, respectively, whereas for Model 2, those were 11.2558, 9.2485, and 36.5991, respectively. As a result, the performance of Model 1 is superior to Model 2, and Model 1 has higher stability and generalization ability, meanwhile demonstrating that Model 1 is practical.