Comfort Evaluation And Moisture Properties Prediction Of Noval Functional Knitted Fabrics

With the development of science and technology, improvement of people’s living standard, functional and comfort are more and more important in fabric research and development. "The 11th five-year plan" aims to develop high-grade functional, differentiate fiber in textile industry. Thus it is very important to develop functional fabric and establish comprehensive, scientific evaluation method.The thesis mainly analyzed the key technology of pearl-cellulose blend fiber knitted fabric’s industrialization; establish heat and moisture evaluation system on knitted fabric for underwear. The comfort of coolmax/cotton double-faced effect knitted fabric are also evaluated and comfort evaluation system on knitted fabric for summer is established either. Moisture transfer properties are taken on these fabric and OMMC predict model based on neural networks is established. The main content and outcomes are as following:First, key technology of pearl-cellulose blend fiber knitted fabric’s industrialization is researched to develop novel functional knitted fabric. Through the data from particle diameter instrument, nano-grade pearl powders are chosen for wet spin silk. Through a series test on pearl-cellulose blend fiber and analysis of function theory, pearl-cellulose blend fiber is proved to have attributes of skin benefit, UV radiation protection and far infrared emission. Based on result of main index and wearing properties on different yarn, pearl/tencel/model blended yarn is proved to have ideal performance and suit to be industrialized. Four different experiments are made to test wicking effect, air permeability, water vapor transmission and thermal insulation of these fabrics. Then gray clustering analyze method is introduced to check and evaluate the comprehensive comfort of these fabrics. Through subjective experiment, comfort properties of underwear during rest and sport stages are analyzed. Pearl/tencel/model is proved to have good air and moisture transfer properties. Based on fuzzy set theory, comfort of different fabric with pearl-cellulose blend fiber is evaluated in four stages of subject test.Then, coolmax/cotton double-faced effect knitted fabrics for summer sportswear are studied. Six test including air permeability experiment, water-vapor permeability experiment, wicking experiment, moisture regain rate experiment, evaporation rate experiment and water retention experiment are chosen as index to evaluate moisture comfort. Based on data of subjective experiment, different comfort property is analyzed in four stages.9 kinds of subjective sensations are abstracted into 3 main factors:tactile factor, heat-moisture factor and pressure factor. Based on experimental data, a multiple regression model for predicting comfort sensations of knitted fabric is established and proved to have good predictability. Based on factor analysis, data of objective experiments includes thermal insulation, touch feeling of warm or cool Q-max, air permeability, water-vapor permeability, wicking, moisture regain rate, evaporation rate experiment are abstracted into 3 main factors:moisture transfer factor, thermal transfer factor and air transfer factor. Based on experimental data, a multiple regression model for predicting comfort sensations of knitted fabric in sports condition with objective properties is established. Because objective experiments is easy to conduct and subjective is difficult to organize, based on Matlab neural networks, a predict model of subjective comfort sensation on sports stage through objective data is established and with high accuracy. The model will help to develop functional fabrics and evaluate their comfort characteristics.Last, dynamic moisture transfer property of functional knitted fabric is analyzed. Based on moisture management tester of Hong Kong Polytechnic University, fabrics are classified into six kinds according to liquid moisture transfer property and the ability and character of managing liquid moisture of different types are described. Through clustering of liquid moisture index, the bottom max absorbing rate (MARb), the bottom spreading speed (SSb) and one-way transport capacity (OWTC) are proved to represent dynamic transfer process of liquid moisture in fabric. The relationships between heat-moisture indexes of objective static tests, dynamic liquid moisture transfer tests and subjective trials are analyzed. The overall moisture management capability (OMMC) can reflect the moisture transfer at sweat when people doing exercise and is close to the true feeling of people. OMMC is proved to agree with moisture comfort result of objective and subjective experiments. Due to many factors affecting OMMC, predict model with neural networks is established. The input are weight, thickness, air permeability experiment, water-vapor permeability experiment, wicking experiment, moisture regain rate experiment, evaporation rate experiment and water retention experiment. The output is OMMC. The correlation coefficient is 0.947, the average error is 4.47% and the accuracy is ideal.