Evaluation and Prediction of the Effect of Fabric Wetting on Coolness

Round 1

Reviewer 1 Report

Comments to the authors

Page 6, 1.1. Materials: the sentence „This study involves the coolness of multiple types of fabrics.“ should have the form „This study involves the thermal contact coolness of multiple types of wetted abrics“. This modification should be respected in the whole manuscript.

 

Page 7, using the TPS 2500S Hot disc thermal constants analyser: several papers published in GOOGLE indicate, that thermal conductivity "lambda" values measured by this instrument provide lambda values differing up to 40% from the lambda values determined by the heat flux sensor based instruments (see ISO 8301), namely for low levels of lambda. As lambda is part of thermal absorptivity, also thermal absorptivity values can be less accurate.  The authors should admit this imperfection.  

 

Page 7, Subjective measurement experiment: instead of „inside of the forearm“, pls write„inner side side of the forearm“.  

 

Page 8, testing principle: thermal feeling felt by a covered forearm for more then 2 second (see pls the Kawabata papers) is already affected by the steady – state heat conduction, which is a function of thermal resistance. In case of wetted fabrics, the cooling flow between the skin and the environment is given by moisture evaporation from the fabric surface. The cooling flow increases with the time and reached its maximum, then (namely for hydrophobic fabrics) the cooling effect drops. There is also the effect of dynamic cooling of the proper fabric. The authors should admit and complete /explain) the complexity of the heat transfer process  analyzed in the paper and made their concusions less optimistic.   

 

Page 9. Table 2: the dimension of thermal absorptivity b [Ws1/2/m2/K] presented in this table is missing.

 

Page 10, Figure 2: the dimension of thermal absorptivity b [Ws1/2/m2/K] is incorrect.

 

Page 10, under Figure 2: the expression „soft“ is unclear and it involves bending rigidity and compressibility.  Pls write „compressibility.

 

Page 13, Random forest model predicts coolness: in fact, the prediction of thermal absorptivity b of wetted fabrics based on any regression model is confusing. First, the b value, specific of a particular fabic, must be determined experimentally. Then,what is the use of the related regression model?  This model cannot provide a b value for any fabric, as it is not a physical model.  

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Author Response

Replies to academic Reviewer:

1. Page 6, 1.1. Materials: the sentence „This study involves the coolness of multiple types of fabrics.“ should have the form „This study involves the thermal contact coolness of multiple types of wetted fabrics“. This modification should be respected in the whole manuscript.

Thanks for the suggestion of the reviewer.

“This study involves the coolness of multiple types of fabrics.” has been changed “This study involves the thermal contact coolness of multiple types of wetted fabrics” in the revised manuscript. 

 

2. Page 7, using the TPS 2500S Hot disc thermal constants analyser: several papers published in GOOGLE indicate, that thermal conductivity "lambda" values measured by this instrument provide lambda values differing up to 40% from the lambda values determined by the heat flux sensor based instruments (see ISO 8301), namely for low levels of lambda. As lambda is part of thermal absorptivity, also thermal absorptivity values can be less accurate. The authors should admit this imperfection

Thanks for the comment of the reviewer.

The opinion you mentioned is very professional, and the instrument error was not considered by me. Mansoor T.'s Alambeta test results in his research were indeed slightly higher than my research results. I will reiterate this limitation in the results. Thank you again for your constructive comment.

References

• Mansoor, T.; Hes, L.; Bajzik, V.; Noman, M.T. Novel method on thermal resistance prediction and thermo-physiological comfort of socks in a wet state. Text. Res. J. 2020, 90, 17–

 

3. Page 7, Subjective measurement experiment: instead of „inside of the forearm“, pls write„inner side side of the forearm“.

Thanks for the suggestion of the reviewer.

“inside of the forearm” has been changed “inner side side of the forearm” in the revised manuscript.

 

4. Page 8, testing principle: thermal feeling felt by a covered forearm for more then 2 second (see pls the Kawabata papers) is already affected by the steady – state heat conduction, which is a function of thermal resistance. In case of wetted fabrics, the cooling flow between the skin and the environment is given by moisture evaporation from the fabric surface. The cooling flow increases with the time and reached its maximum, then (namely for hydrophobic fabrics) the cooling effect drops. There is also the effect of dynamic cooling of the proper fabric. The authors should admit and complete /explain) the complexity of the heat transfer process analyzed in the paper and made their conclusions less optimistic.   

Thanks for the comment of the reviewer.

We are well aware that prolonged contact between skin and fabric can affect the human body's judgment of the coolness of the fabric. Therefore, in subjective testing, we clearly conveyed to the subjects the need to evaluate the heat sensation of the fabric in its first contact. Of course, subjective experiments are difficult to achieve absolute accuracy. I will reinterpret this phenomenon in the results and acknowledge the limitations of the experiment.

 

5. Page 9. Table 2: the dimension of thermal absorptivity b [Ws1/2/m2/K] presented in this table is missing.

Thanks for the suggestion of the reviewer.

The dimension of thermal absorptivity b has been added in the revised manuscript.

 

6. Page 10, Figure 2: the dimension of thermal absorptivity b [Ws1/2/m2/K] is incorrect.

Thanks for the suggestion of the reviewer.

The dimension of thermal absorptivity b has been corrected in the revised manuscript.

 

7. Page 10, under Figure 2: the expression „soft“ is unclear and it involves bending rigidity and compressibility.  Pls write „compressibility.

Thanks for the suggestion of the reviewer.

“soft” has been changed “compressibility” in the revised manuscript.

 

8. Page 13, Random forest model predicts coolness: in fact, the prediction of thermal absorptivity b of wetted fabrics based on any regression model is confusing. First, the b value, specific of a particular fabic, must be determined experimentally. Then,what is the use of the related regression model?  This model cannot provide a b value for any fabric, as it is not a physical model.  

Thanks for the comment of the reviewer.

At present, research on subjective evaluation of fabric properties often uses machine learning to predict, because many fabric properties testing is very time-consuming and labor-intensive. As an example of this study, wet fabrics are inevitably affected by evaporation when measuring their heat absorption rate. At the same time, the objective results of the measurement also differ from the actual subjective feelings. If all subjective tests are used, the engineering quantity is also huge. Therefore, machine learning can play a certain role in subjective evaluation of fabric characteristics, and it is a research hotspots in this field.

We have also described the relevant theoretical models in other papers

References

• Wu, Z.; Shi, Y.; Yang, R.; Qian, X.; Fang, S. Modification and Validation of a Dynamic Thermal Resistance Model for Wet-State Fabrics. Processes 2023, 11, 1630.

Reviewer 2 Report

The current research paper entitled “Evaluation and prediction of the effect of fabric wetting on coolness” focus on the prediction of fabric coolness using a random forest regression model. I found the paper well-studied and may provide new information on the utilization of random forest regression models for clothing comfort. However, the paper needs further revision before accepting for publication due to the following reasons.  

1.      The current work’s uniqueness is not clearly stated in the manuscript.

2.      The introduction part has been written vaguely and lengthy. What is the purpose of the fourth paragraph in the introduction section under the subtitle of methods? Is it part of the introduction part? I suggest the introduction part should be revised. Moreover, the author/s should provide a research gap of at the end of the introduction section.

3.      In the experimental section the author/s explained ‘When the fabric is made of two types of fibers blended together, the blended fiber density cannot be directly obtained. Therefore, it can be estimated using the following equation proposed by Militky [23].’ Which formula? Please provide it.

4.      Figure 1 has quite poor quality. Please change it.

5.      Please re-check the fabric porosity result. It seems unrealistic.

6.      The authors should provide clear fabric information In Table 1. For example, the fabric structure was twill and knit. Which type of twill and knit? Please do not use a generic name.

7.      Thread density of the fabric is an important factor for comfort properties. Please provide it.

8.      The authors classified fabric coolness into five levels based on their thermal absorption coefficient value. However, any reference or earlier research report is not cited for this classification.

9.      Page 9, According to the fuzzy comprehensive evaluation method….please specify the test method number if it is international or national standard.

10.   In Table 2, the measurement unit is missing.

11.   Page 10, line 14. …….The subjective evaluations of the subjects are shown in the table. Which table?

12.   Thermal absorption coefficient results of different fabrics are not well explained and discussed scientifically. You may refer to the research paper on different fabrics of thermal absorption coefficient behavior, Fibers Polymers 23, 1150–1160 (2022), https://doi.org/10.1007/s12221-022-4160-x

13.   The conclusion part must be revised using quantitative findings.

14.   Most of your references are old. Please remove them and substitute them with recent papers published within the last five years. Consider referring to the following

-        Textile research journal,2022,  https://doi.org/10.1177/00405175221095891

-        Materials 2021, 14(22), 6863; https://doi.org/10.3390/ma142268633

-        Journal of Natural Fibers 2022, 19 (13), https://doi.org/10.1080/15440478.2021.1875351

 

-        Fibers Polymers 2022, 23, 3297–3308. https://doi.org/10.1007/s12221-022-4025-3

  I observed some typos and grammatical errors. Please improve the use of English. 

Author Response

Replies to Academic Editor:

1. The current work’s uniqueness is not clearly stated in the manuscript.

Thanks for the suggestion of the reviewer.

The innovation of the research has been added in the revised manuscript.

 

2. The introduction part has been written vaguely and lengthy. What is the purpose of the fourth paragraph in the introduction section under the subtitle of methods? Is it part of the introduction part? I suggest the introduction part should be revised. Moreover, the author/s should provide a research gap of at the end of the introduction section.

Thanks for the suggestion of the reviewer.

The introduction of the paper has been revised in the revised manuscript.

 

3. In the experimental section the author/s explained ‘When the fabric is made of two types of fibers blended together, the blended fiber density cannot be directly obtained. Therefore, it can be estimated using the following equation proposed by Militky [23].’ Which formula? Please provide it.

Thanks for the suggestion of the reviewer.

ρ_{ab=rρa+(1-r)ρb}

Militky formula has been added in the revised manuscript.

 

4. Figure 1 has quite poor quality. Please change it.

Thanks for the suggestion of the reviewer.

Figure 1 has been changed in the revised manuscript.

 

5. Please re-check the fabric porosity result. It seems unrealistic.

Thanks for the suggestion of the reviewer.

The porosity is a theoretical value calculated based on the formula.

ρ_{fab=m/h ；  ε0=1-ρfab/ρfib}

Where ρ_fab is the fabric density under the metric moisture return rate [g/m³]. ρ_fib is the fiber density under the metric moisture return rate [g/m³]. m is the measured surface density of the fabric [g/m³]. h is the measured thickness of the fabric [m]. ε₀ is the dry fabric surface porosity [%].

 

References

[1]Mangat MM, Hes L and Bajzı´k V. Thermal resistance models of selected fabrics in wet state and their experimental verification. Text Res J 2015; 85: 200-210.

 

6. The authors should provide clear fabric information In Table 1. For example, the fabric structure was twill and knit. Which type of twill and knit? Please do not use a generic name.

Thanks for the suggestion of the reviewer.

Detailed fabric structure has been added in the revised manuscript.

 

7. Thread density of the fabric is an important factor for comfort properties. Please provide it.

Thanks for the suggestion of the reviewer.

Thread density of the fabric has been added in the revised manuscript.

 

8. The authors classified fabric coolness into five levels based on their thermal absorption coefficient value. However, any reference or earlier research report is not cited for this classification.

Thanks for the comment of the reviewer.

In order to quantify subjective thermal sensation, the ASHRAE thermal sensation scale is usually used to classify the subjective sensation of the human body. At present, there are mainly three, five, and seven point scales used for thermal sensation. In this study, the fabric cooling sensation is classified into five grades based on the five point scale.

References

[2]ASHRAE. ASHRAE handbook: fundamentals. 2023.

 

9. Page 9, According to the fuzzy comprehensive evaluation method….please specify the test method number if it is international or national standard.

Thanks for the comment of the reviewer.

Fuzzy comprehensive evaluation method is a comprehensive evaluation method based on fuzzy mathematics, which is a mathematical classification method. Unfortunately, no relevant standards can be found at present.

 

10. In Table 2, the measurement unit is missing.

Thanks for the suggestion of the reviewer.

The dimension of thermal absorptivity b has been added in the revised manuscript.

 

11. Page 10, line 14. …….The subjective evaluations of the subjects are shown in the table. Which table?

Thanks for the suggestion of the reviewer.

This error has been corrected in the revised manuscript.

 

12. Thermal absorption coefficient results of different fabrics are not well explained and discussed scientifically. You may refer to the research paper on different fabrics of thermal absorption coefficient behavior, Fibers Polymers 23, 1150–1160 (2022), https://doi.org/10.1007/s12221-022-4160-x.

Thanks for the comment of the reviewer.

The conclusion section has added a discussion on the thermal absorption coefficient in the revised manuscript.

“The thermal absorption coefficient of 100% cotton fabric under wet conditions is high, ranging from 112.72 to 455.97 Ws1/2/(m2·℃), while the thermal absorption coefficient of 98% polyester + 2% elastane blend fabric under wet conditions is low, ranging from 85.89 to 331.11 Ws1/2/(m2·℃).This is because the 100% cotton fabric has more water absorption than the 98% polyester+2% elastane blend fabric, resulting in stronger contact cooling.”

 

13. The conclusion part must be revised using quantitative findings.

Thanks for the comment of the reviewer.

Specific results have been added to the conclusion section in the revised manuscript.

 

14. Most of your references are old. Please remove them and substitute them with recent papers published within the last five years. Consider referring to the following.

Thanks for the comment of the reviewer.

The revised manuscript has replaced the latest references.

Reviewer 3 Report

Reviewer Comments:

In this research work author explore the cool feeling of fabrics at different humidities, authors tested the heat transfer between fabrics and skin for 20 different fabrics with varying heat absorption rates, using a fuzzy comprehensive evaluation to objectively assess their coolness levels. Subjective evaluation was obtained by having subjects touch the fabrics and provide feedback, resulting in a subjective evaluation of their coolness level. Ultimately, authors also compared the objective and subjective evaluations and found them to be highly consistent (R²=0.909), indicating accurate objective classification of fabric coolness levels. Currently, random forest regression models are widely used in the textile industry for classification, identification, and performance prediction. These models enable the prediction of fabric coolness levels by simultaneously considering the impact of all fabric parameters. Author established a random forest regression model to predict the coolness of wet fabrics, obtaining high accuracy between predicted and tested thermal absorption coefficients (R²=0.872, RMSE=0.305). Although the R² values are ok but still the data in not enough. The authors have not mentioned basic characterizations such as SEM, TGA or FTIR to explore the results with proper reasoning. I would not recommend this paper for publishing in journal of Processes.

Comments:

1.     Many researchers have studied the Evaluation and prediction of the effect of fabric wetting on coolness Like “Tang, K. P. M., Kan, C. W., & Fan, J. T. (2015). Assessing and predicting the subjective wetness sensation of textiles: subjective and objective evaluation. Textile Research Journal, 85(8), 838-849”. It is suggested to highlight the novelty of this work.

2.     Author have not mentioned any characterization such as optical images, SEM, TGA, FTIR.

3.     Authors have also not mentioned any proper reasoning for heat absorption rate.

4.     Authors explained that there are two factors that affect the cooling effect such as cooling factors and environmental factors. Please explain which factor is dominant and why ?

5.     How we can use the textile thermal resistance tester or on which principle does it work?

6.     Keep in view cooling effect which other properties author feel to target to design a model, authors mention that results indicate that water content is the most important factor affecting the coolness of fabrics. Please explain why?

7.     The authors are suggested to double-check the whole manuscript for grammar errors and typos that need to be corrected.

 

 

Author Response

Replies to Academic Editor:

1. Many researchers have studied the Evaluation and prediction of the effect of fabric wetting on coolness Like “Tang, K. P. M., Kan, C. W., & Fan, J. T. (2015). Assessing and predicting the subjective wetness sensation of textiles: subjective and objective evaluation. Textile Research Journal, 85(8), 838-849”. It is suggested to highlight the novelty of this work.

Thanks for the comment of the reviewer.

The innovation of the research has been emphasized in the revised manuscript.

“In this study, to more accurately classify the coolness level of fabrics in the wet state and establish a fabric prediction model to evaluate the contact coolness of fabrics with different water contents. The study tested the thermal absorption coefficients of 20 types of fabrics at different water content levels. Then, the fuzzy comprehensive evaluation method was used to study the cooling sensation level of the fabric. Finally, a comparison was made with the subjective evaluation level of the subjects to determine the cooling sensation level that is in line with the actual use experience. Based on objective parameter evaluation characteristics and human sensory evaluation results, a Random Forest regression model was established to effectively predict the coolness of fabrics.”

 

2. Author have not mentioned any characterization such as optical images, SEM, TGA, FTIR.

Thanks for the comment of the reviewer.

This study focuses on the changes in contact coolness sensation of fabrics after wetting, and no optical characterization related experiments have been found in the references, so there is no optical characterization of the fabric surface. In the next study of the impact of fabric surface structure and finishing on fabric coolness, we will add optical experiments.

 

3. Authors have also not mentioned any proper reasoning for heat absorption rate.

Thanks for the comment of the reviewer.

The conclusion section has added a discussion on the thermal absorption coefficient in the revised manuscript.

“The thermal absorption coefficient of 100% cotton fabric under wet conditions is high, ranging from 112.72 to 455.97 Ws1/2/(m2·℃), while the thermal absorption coefficient of 98% polyester + 2% elastane blend fabric under wet conditions is low, ranging from 85.89 to 331.11 Ws1/2/(m2·℃). This is because the 100% cotton fabric has more water absorption than the 98% polyester + 2% elastane blend fabric, resulting in stronger contact coolness.”

 

4. Authors explained that there are two factors that affect the cooling effect such as cooling factors and environmental factors. Please explain which factor is dominant and why ?

Thanks for the comment of the reviewer.

The coolness of fabrics is mainly determined by their own factors and environmental factors. Since this article only explores the impact of fabric's own factors on contact coolness under the same environmental conditions, it was found that moisture content has a significant impact on fabric coolness. Therefore, it is impossible to determine which of these two factors is more dominant. In future research, I will consider studying the differences in fabric coolness under different environmental conditions.

 

5. How we can use the textile thermal resistance tester or on which principle does it work?

Thanks for the comment of the reviewer.

The working principle of the instrument is：

“The instrument needed to be turned on in advance and preheated for 30 minutes. The probe temperature was set to 35℃, the same as the temperature of human skin, and after contact with the sample, the resistance of the probe changes. Based on the change in probe resistance, the thermal conductivity and thermal absorption coefficient of the fabric can be accurately calculated. Before each test, the sample had to be in the same thermal state, and consecutive measurements were not allowed. Adequate time was required to ensure that the previously measured samples returned to their initial thermal state”.

The above content has been added to the revised manuscript.

 

6. Keep in view cooling effect which other properties author feel to target to design a model, authors mention that results indicate that water content is the most important factor affecting the coolness of fabrics. Please explain why?

Thanks for the comment of the reviewer.

Residual mean square is a method to measure the importance of Random forest characteristics by measuring the reduction of random replacement residual mean square. The residual mean square is used to evaluate the importance of influencing factors of fabric contact cooling sensation. Through the importance analysis of influencing factors of the model, it is found that the water content is the most significant among the characteristic parameters (score=0.188).

This is because the thermal conductivity(λ) of water is generally 2-6 times higher than that of fiber polymers. The volumetric heat capacity (c) of water is generally 1.3-3 times higher than that of fiber polymers. Therefore, according to the equation (1) the thermal absorption coefficient of the fabric increases significantly after wetted.

                           b=(λρc)^1/2

7. The authors are suggested to double-check the whole manuscript for grammar errors and typos that need to be corrected.

Thanks for the comment of the reviewer.

Grammar errors and typos has been corrected in the revised manuscript.

Round 2

Reviewer 1 Report

No comments, the reviewer proposals of amendments were mostly respected, others comments explained. 

Author Response

We would like to express our sincere thanks to the reviewers for the constructive and positive comments.

Reviewer 2 Report

Can be accept.

 

Author Response

We would like to express our sincere thanks to the reviewers for the constructive and positive comments.

Reviewer 3 Report

Reviewer Comments:

In this research work author explore the cool feeling of fabrics at different humidities, authors tested the heat transfer between fabrics and skin for 20 different fabrics with varying heat absorption rates, using a fuzzy comprehensive evaluation to objectively assess their coolness levels. Subjective evaluation was obtained by having subjects touch the fabrics and provide feedback, resulting in a subjective evaluation of their coolness level. Ultimately, authors also compared the objective and subjective evaluations and found them to be highly consistent (R²=0.909), indicating accurate objective classification of fabric coolness levels. Currently, random forest regression models are widely used in the textile industry for classification, identification, and performance prediction. These models enable the prediction of fabric coolness levels by simultaneously considering the impact of all fabric parameters. Author established a random forest regression model to predict the coolness of wet fabrics, obtaining high accuracy between predicted and tested thermal absorption coefficients (R²=0.872, RMSE=0.305). Although the R² values are ok but still the data in not enough. I would recommend this paper for publishing in journal of Processes with some minor chnages.

Comments:

1.     Section numbers are not consistent. Why R² value is low?

2.     For proper understanding author should design a schematic model for different reasoning behind change in cool feeling.

3.     The authors are suggested to double-check the whole manuscript for grammar errors and typos that need to be corrected.

Author Response

We would like to express our sincere thanks to the reviewers for the constructive and positive comments.

Replies to academic Reviewer :

1. Section numbers are not consistent. Why R2 value is low?

Thanks for the comment of the reviewer.

The section numbers have been reorganized in the revised manuscript.

Due to the relatively small amount of data obtained in the study, this may be the main reason for the low R² value, and this limitation is also explained in the conclusion.

2. For proper understanding author should design a schematic model for different reasoning behind change in cool feeling.

Thanks for the comment of the reviewer.

After wetting, the fabric system becomes very complex, and there are four main interactions between water and the fabric,

(1) Hydraulic fibers create strong hydrogen bonds with water molecules.

(2) Microporus organization of fibers with polarity allows water molecules to Penerate.

(3) Water molecules reach macro pores due to the fabric's weave, the set of Yarns, and their finesse.

(4) Superficial attachment on the surface of the fabric.

As the moisture content of the fabric increases, all four of the above effects are possible, and the specific binding method between water and the fabric is difficult to predict. Therefore, the coolness sensation of the fabric after wetting is difficult to express through simple schematic diagrams. I'm very sorry.

3. The authors are suggested to double-check the whole manuscript for grammar errors and typos that need to be corrected.

Thanks for the suggestion of the reviewer.

I have proofread the grammar and spelling of the entire text again.