Study On Influencing Factors For Heat-moisture Comfort Of Children’s Clothing

The market for children’s clothing in our country has great potential,but the children’s clothing on the market tends to be adult.And the heat-moisture comfort of children’s clothes is poor.Early childhood is a special period of growth and development,the quality of clothing not only affect the growth and development of children,their psychological growth,but also influence their character.Although the research on the comfort of children’s clothing has been increasing year by year,few researches have been done on the clothing of children’s clothing.Among these papers,fewer focus on the comfort of heat-moisture.Therefore,no matter from market or academic perspective,it has a great significance to study on children’s clothing heat-moisture comfort factors.The objective and subjective indexes of heat-moisture comfort are obtained by the testes mainly started from two aspects:fabric and clothing.From the three aspects of fabric,pattern and style,objective and subjective experiments are finished to get different indexes of heat-moisture comfort.The heat and moisture comfort related indicators,including 9 objective indicators:the thickness of the fabric,water content,drying rate,thermal resistance,clo,thermal conductivity,Wet resistance,moisture permeability index and moisture permeability,are measured from four common fabrics in different environmental conditions.At the same time,invited 12 professional people who majored textile&fabric to measure the grade in 5 degrade.The common contents are breathability,smoothness,thickness,feeling of hot,feeling of cold,feeling of sticky,feeling of cold after wet,feeling of sticky after wet.Respectively using the four kinds of fabric to make two samples of different degree of loose,invited 12 children to try it on,using infrared imaging instrument testing garment surface temperature,the test time of sweat and sweat parts number understanding of clothing thermal comfort objective indicators.At the same time,by asking to understand children’s clothing breathable feeling when wearing clothing,smooth feeling,thick feeling,hot feeling,gooey feeling cold feeling,contact,cold,wet after wet level 5 ratings of the gooey feeling.Base on the data,GRNN neural network was established by Matlab to predict the heat-moisture comfort of clothing.Through the above experiments,the following results can be obtained:1)In the same environment,the coefficient K is obtained by experiments that show the fabric thermal resistance,wet resistance and thickness are the main factors that affect the heat-moisture comfort,the rest of the factors can be represented by these three factors.In different environment,six indicators of fabric thermal and humidity comfort,the temperature,humidity,wind speed;fabric thermal resistance,wet resistance,thickness are important factors to obtain a measure coefficient K’.The thermal resistance,wet resistance,the number of layers are important factors affecting the comfort of the fabric,get the coefficient of measurement K.After comparative analysis,the thermal comfort of four fabrics.2)Factor analysis shows that the opening,fabrics,clothing loose degrees and coverage is the important influence factors of clothing heat-moisture comfort,combined with formula of fabrics,concluded formula of C that judge the comfort of children clothing:C（28）12.9 3%T（10）11.06%H（10）.646%V_w（10）.439%R_ct（10）.340%R_et（10）.320%m（10）38.25%s（10）15.28%o（10）.503%e In the high temperature and humidity environment,the larger C gets,the more comfortable clothing is.3)The consistent analysis shows that children’s subjective evaluation is consistent with the subjective evaluation of professionals’.Children’s evaluation is creditable.Through the analysis of subjective evaluation of relevant indicators,it is concluded that subjective evaluation is consisitent with objective evaluation.4)Using Matlab to establish multiple GRNN neural network,a single GRNN neural network,the BP neural network to predict comfort index C.The prediction shows that the prediction accuracy of a single GRNN neural network is the best,and the order of the prediction results is consistent with the order of the actual values.