Sage Journals: Discover world-class research

Abstract

The utilization of shape memory alloy (SMA) in shape memory fabric (SMF) has revolutionized thermal protective clothing, significantly enhancing its thermal protection. However, the cost- and time-consuming process of SMA shape memory training and performance testing can be optimized for improved efficiency. This study addresses this challenge by developing machine learning models to predict the thermal protection of a smart fabric system (SFS) with a SMF. The training data was sourced from the previous experimental studies, and six features significantly impacting thermal protection were identified. Results demonstrated that gradient boosting regressor (GBR) model exhibited the highest accuracy, with the SMA interval emerging as the most critical feature in determining thermal protection. Moreover, the GBR model predicted that SFS presented the best thermal protection when the dry SMF was woven by SMA of 2 cm interval and aramid 1414 of 20 roots/cm density, located between the moisture barrier and thermal liner vertically.

Keywords

machine learning thermal protective performance shape memory alloy smart fabric

Get full access to this article

View all access options for this article.

References

Ahmad

M. W.

Mourshed

Rezgui

(2017). Trees vs neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings, 147(7), 77–89. https://doi.org/10.1016/j.enbuild.2017.04.038

American Burn Association. (2019). National burn repository 2019 update: Report of data from 2009–2018. https://sk75w2kudjd3fv2xs2cvymrg-wpengine.netdna-ssl.com/wp-content/uploads/2020/05/2019-ABA-Annual-Report_FINAL.pdf

Bartkowiak

Dabrowska

Greszta

(2020). Development of smart textile materials with shape memory alloys for application in protective clothing. Materials, 13(3), 1–17. https://doi.org/10.3390/ma13030689

Bartkowiak

Dabrowska

Okrasa

Wloch

(2017). Preliminary study on the thermomechanical treatment of shape memory alloys for applications in clothing protecting against heat. Advances in Mechanical Engineering, 9(6), 1–9. https://doi.org/10.1177/1687814017703898

Chen

J. Q.

Hsin

Dai

W. J.

Qiu

Calvin

Y. C.

(2019). Artificial intelligence approach to find lead compounds for treating tumors. The Journal Of Physical Chemistry Letters, 10(15), 4382–4400. https://doi.org/10.1021/acs.jpclett.9b01426

Cheng

Feng

X. M.

Wick

Peters

G. Q.

(2021). Machine learning assisted discovery of new thermoset shape memory polymers based on a small training dataset. Polymer, 214(1), 123351. https://doi.org/10.1016/j.polymer.2020.123351

Congalton

(1999). Shape memory alloys for use in thermally activated clothing, protection against flame and heat. Fire and Materials, 23(5), 223–226. https://doi.org/10.1002/(SICI)1099-1018(199909/10)23:5<223::AID-FAM687>3.0.CO;2-K

Cui

Z. Y.

Yang

H. Y.

(2011). Modeling thermal protective performance of multilayer fabrics for firefighters. Journal of Donghua University, 28(3), 271–274. https://doi.org/CNKI:SUN:DHDY.0.2011-03-010

Fawagreh

Gaber

M. M.

Elyan

(2014). Random forests: From early developments to recent advancements. Systems Science and Control Engineering, 2(1), 602–609. https://doi.org/10.1080/21642583.2014.956265

10.

Gök

M. O.

Bilir

M. Z.

Gürcüm

B. H.

(2015). Shape-memory applications in textile design. Procedia Social and Behavioral Sciences, 195(7), 2160–2169. https://doi.org/10.1016/j.sbspro.2015.06.283

11.

J. Z.

Y. H.

Wang

L. J.

N. N.

(2018). On the improvement of thermal protection for temperature-responsive protective clothing incorporated with shape memory alloy. Materials, 11(10), 1932. https://doi.org/10.3390/ma11101932

12.

Islam

S. R.

W. D.

Naveed

(2019). Influence of silica aerogels on fabric structural feature for thermal isolation properties of weft-knitted spacer fabrics. Journal of Engineered Fibers and Fabrics, 14(7), 514–519. https://doi.org/10.1177/1558925019866446

13.

Koh

J. S.

(2018). Design of shape memory alloy coil spring actuator for improving performance in cyclic actuation. Materials, 11(11), 2324. https://doi.org/10.3390/ma11112324

14.

Lawson

L. K.

Crown

E. M.

Ackerman

M. Y.

Dale

J. D.

(2004). Moisture effects in heat transfer through clothing systems for wildland firefighters. International Journal of Occupational Safety and Ergonomics, 10(3), 227–238. https://doi.org/10.1080/10803548.2004.11076610

15.

Lecun

Bengio

Hinton

(2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539

16.

Liu

Xiao

A. W.

Lei

G. Y.

Dong

G. F.

M. T.

(2020). Intelligent predicting of salt pond’s ion concentration based on support vector regression and neural network. Neural Computing and Applications, 32(22), 16901–16915. https://doi.org/10.1007/s00521-018-03979-9

17.

Liu

X. H.

Tian

Wang

Y. Y.

(2021). Modeling to predict thermal aging for flame-retardant fabrics considering thermal stability under fire exposure. Textile Research Journal, 91(21–22), 2656–2668. https://doi.org/10.1177/00405175211013835

18.

Y. H.

Song

G. W.

Wang

F. M.

(2015). Performance study of protective clothing against hot water splashes: From bench scale test to instrumented manikin test. Annals of Occupational Hygiene, 59(2), 232–242. https://doi.org/10.1093/annhyg/meu087

19.

Y. H.

Wang

L. J.

J. Z.

P. J.

Song

W. F.

(2022). Investigation of the thermal protective performance of shape memory fabric system: Effect of moisture and position of shape memory alloy. Clothing and Textile Research Journal, 40(1), 73–86. https://doi.org/10.1177/0887302X20937075

20.

N. N.

Y. H.

J. Z.

Dai

H. Q.

(2019). Application of shape memory materials in protective clothing: A review. Journal of the Textile Institute, 110(6), 950–958. https://doi.org/10.1080/00405000.2018.1532783

21.

N. N.

Wang

L. J.

Y. H.

Dai

H. Q.

(2017). Thermal protection of fireproof fabrics with shape memory alloy springs under hot surface contact. In Li

W. L.

(Eds.), TBIS 2017. Proceedings of 10th textile bioengineering and informatics symposium (pp. 752–758). Textiles Bioengineering and informatics Society.

22.

Malhotra

Biswas

Chen

F. C.

Sharma

G. D.

(2021). Prediction of non-radiative voltage losses in organic solar cells using machine learning. Solar Energy, 228(5), 175–186. https://doi.org/10.1016/j.solener.2021.09.056

23.

Mandal

Annaheim

Greve

Camenzind

Rossi

R. M.

(2019). Modeling for predicting the thermal protective and thermo-physiological comfort performance of fabrics used in firefighters’ clothing. Textile Research Journal, 89(14), 2836–2849. https://doi.org/10.1177/0040517518803779

24.

Mandal

Song

G. W.

(2014). An empirical analysis of thermal protective performance of fabrics used in protective clothing. Annals of Occupational Hygiene, 58(8), 1065–1077. https://doi.org/10.1093/annhyg/meu052

25.

McQuerry

DenHartog

Barker

(2017). Evaluating turnout composite layering strategies for reducing thermal burden in structural firefighter protective clothing systems. Textile Research Journal, 87(10), 1217–1225. https://doi.org/10.1177/0040517516651101

26.

Michalak

Krucińska

(2016). A smart fabric with increased insulating properties. Textile Research Journal, 86(1), 97–111. https://doi.org/10.1177/0040517515581585

27.

Pan

M. J.

Wang

L. J.

Y. H.

J. X.

Liu

S. Y.

(2023). Design and fabrication of NiTi shape memory alloy/aramid composite fabric for thermal protective clothing. Smart Materials and Structures, 32(5), 055003. https://doi.org/10.1088/1361-665X/acc3e4

28.

Ranstam

Cook

J. A.

(2018). Lasso regression. British Journal of Surgery, 105(10), 1348. https://doi.org/10.1002/bjs.10895

29.

Rossi

R. M.

Bolli

W. P.

(2005). Phase change materials for improvement of heat protection. Advanced Engineering Materials, 7(5), 368–373. https://doi.org/10.1002/adem.200500064

30.

Sun

Yoo

H. S.

Zhang

X. S.

Pan

(2000). Radiant protective and transport properties of fabrics used by wildland firefighters. Textile Research Journal, 70(7), 567–573. https://doi.org/10.1177/004051750007000702

31.

Udayraj Talukdar

Das

Alagirusamy

(2017). Effect of structural parameters on thermal protective performance and comfort characteristic of fabrics. Journal of the Textile Institute, 108(8), 1430–1441. https://doi.org/10.1080/00405000.2016.1255123

32.

Udayraj Wang

F. M

. (2018). A three-dimensional conjugate heat transfer model for thermal protective clothing. International Journal of Thermal Sciences, 130(2), 28–46. https://doi.org/10.1016/j.ijthermalsci.2018.04.005

33.

Wang

L. J.

Y. H.

J. Z.

(2020). On the effectiveness of temperature-responsive protective fabric incorporated with shape memory alloy (SMA) under radiant heat exposure. Clothing and Textile Research Journal, 38(3), 212–224. https://doi.org/10.1177/0887302X19892095

34.

Wang

L. J.

Y. H.

Wang

N. N.

(2018). Influence of size of shape memory alloy on thermal protection of fabrics used in firefighters’ protective clothing. Journal of Textile Research, 39(6), 113–118. https://doi.org/10.13475/j.fzxb.20170703906

35.

Wang

L. J.

Pan

M. J.

Y. H.

Song

W. F.

Liu

S. Y.

(2022). Developing smart fabric systems with shape memory layer for improved thermal protection and thermal comfort. Materials and Design, 221(9), 110922. https://doi.org/10.1016/j.matdes.2022.110922

36.

Wang

Y. H.

Wang

L. J.

You

S. Y.

(2018). Effect of thermal hazards on the thermal protection of fabrics used in firefighters’ protective clothing incorporated with shape memory alloy. Journal of Textile Research, 44(1), 76–82. https://doi.org/10.3969/j.issn.1671-0444.2018.01.011

37.

White

J. P.

(2012). An experimental analysis of firefighter protective clothing: the influences of moisture and a thermally activated expanding air-gap [Master’s thesis]. University of Maryland.

38.

Yoo

Yeo

Hwang

Kim

Y. H.

Hur

S. G.

Kim

(2008). Application of a NiTi alloy two-way shape memory helical coil for a versatile insulating jacket. Materials Science and Engineering, 481(3), 662–667. https://doi.org/10.1016/j.msea.2006.12.233

Predicting the Thermal Protective Performance of Smart Fabric Systems With a Shape Memory Layer Using Machine Learning Models

Abstract

Keywords

Get full access to this article

References