Fashion Institute of Design, Donghua University,Shanghai
Application of Genetic Algorithm to Study the Comfort of Waterproof Breathable Fabric Weiyuan zhang,Yan liu,Xiangling Meng Donghua University,Shanghai,2006 2007 Beltwide Cotton Conferences "Equipping for Excellence"
Abstract An appraisal model based on genetic algorithms has been developed and applied to the comfort of different kinds of waterproof breathable fabrics on the basis of subjective and objective experimental measurements. Comparison of the results with real tests shows excellent agreement. Using the appraisal model we can predict the comfort class of garment made of different kinds of breathable waterproof fabrics in different dressing conditions based on the objective testing indexes.
This paper Two indexes thermal insulating value and moist insulating value. used in the objective tests, thermal insulating value and moist insulating value. Three wear trials: Resting, ExercisingRainy Resting, Exercising and Rainy Procedures Fabrics properties may not really reflect their final performance on the garments,Three wear trial permit testing more closely to actual wear. three mathematical simulations of comfort evaluation Develop three mathematical simulations of comfort evaluation of waterproof breathable by Genetic Algorithm
Experimental Objective Experimental Materials ( Table 1 to table 3 ) Five high density fabrics Four coated fabrics Five laminated fabrics One impermeable fabric Experimental conditions: a climate chamber Temperature : 20 ℃ Relative humidity : 35% Air velocity : 0.1m/s Experimental methods JISL——WVP Water vapor penetration LFY-217B test water pressure Results
Table1 Characteristic of high density experimental fabrics Table 2 characteristic of coated experimental fabrics Objective Experimental
Table 3 characteristic of laminated experimental fabrics Objective Experimental
Results of objective experimental Table 4 Testing Results of experimental fabrics
Fig 1 Testing results （ I t ） of experimental fabrics Fig 1 Shows that three types of waterproof breathable experimental fabrics have similar heat transfer property. Comparing Results
Fig 2 Testing results （ i m ） of experimental fabrics Fig 2 shows the moisture transfer property ranks as follows High Density > Laminated > Coated High Density > Laminated > Coated Comparing Results
Fig 3 Testing results （ WVP ） of experimental fabrics Fig 3 shows the water vapor transfer property ranks as follows High Density > Laminated > Coated High Density > Laminated > Coated Comparing Results
Fig 4 Testing results （ p ） of experimental fabrics Fig 4 shows the waterproof ability ranks as follows Laminated > Coated > High Density Comparing Results
Subjective experimental Materials Seven testing garments were made of waterproof breathable fabrics shown in table 1 to 3 Experimental process Put on the assigned raincoat Enter the climate chamber Appraisal Results Sit 10m Run 10m Expose to the rain 10m rate
Results of subjective experimental Table 6 Appraisal Results of testing garments Table 5 Appraisal ruler(thermal,wetness and overall comfort) 1: extremely discomfort; 5: extremely comfort 123 4 5
Comfort Model Introduction of Genetic Algorithms Comfort Model Appraise Model Evaluation of the Appraise Model
Comfort Model Introduction of Genetic Algorithms Global optimization methods based on several metaphors from biological evolutions Comfort Model Program was written in Visual Basic 6.0 Input patterns I t i m WVP and P Output patterns The comfort class
Appraise model Use symbols defined as following x f y x F x F f x F y F ff （1）（1） （2）（2）（3）（3） （4）（4）
Fig.5 Appraise Model for Resting Procedure d ca a b c F f7 f6 f5 f1f8 f7 f4 Fig.6 Appraise Model for Running Procedure a F f6f8f9f6f4 f8 f4 f5
Fig. 7 Appraise Model for Rainy Procedure d c b a F f6 f5 f4 f8 f5 f3 f9 f4 f8
Evaluation of the Appraise Model Six garments made of other samples Subjective appraisal Subjective appraisal with same testing procedure and experimental condition. Comparison Comparison of the predict comfort class with real tests (Fig.8,9,10) Correlation Correlation between real test and model predict at three states (Fig 11 & Table 8)
Fig.8 Comparison of predicted results with real subjective appraisal in resting procedure Comparison of the predict comfort class with real tests in resting
Fig.9 comparison of predicted results with real subjective appraisal in running procedure Comparison of the predict comfort class with real tests in running
Fig.10 Comparison of predicted results with real subjective appraisal in rainy procedure Discussion excellent agreement Comparison of the predict comfort class with real tests shows excellent agreement Comparison of the predict comfort class with real tests in rainy
real testmodel predict(resting)model predict(running)model predict(rainy) Pearson Correlation 1.910(*).958(**).963(**) Sig. (2-tailed)..012.003.002 N 6666 Table 8 correlation between real test and model predict at three states Discussion correlation significant Obviously, the correlation between real test and model predict is significant at three states, that is resting, running and rainy. Fig.11 The scatter plot of the appraisal values between real test and model predict
Conclusions the genetic algorithms proposed in this paper offered a satisfactory model From the comparison of predicted results with real subjective appraisal in three testing procedure, we can make a conclusion that the genetic algorithms proposed in this paper offered a satisfactory model to study the comfort of three types of waterproof breathable fabrics. same testing garment got different comfort evaluation result in different testing procedure The following conclusions emerged as a result test that same testing garment got different comfort evaluation result in different testing procedure, which indicates the relationship between clothing comfort and wear condition.
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