Application of Neural Network for the Prediction of Loss in Mechanical Properties of Aramid Fabrics After Thermal Aging

Year 2024, Volume: 34 Issue: 1, 77 - 86, 31.03.2024

Banu Özgen Keleş

https://doi.org/10.32710/tekstilvekonfeksiyon.1280482

Abstract

Aramid fabrics are used to produce most of the flame resistant protection clothes to fulfil the protection requirements. Even though aramid fibers have good thermal stability and flame resistance properties, fabrics used in protective clothing age and loss some of their essential functions under various environmental and operational conditions during their lifetime. These conditions cause serious limitations in the use of clothing. In this study, various woven fabrics produced from aramid (Nomex, Kevlar) fabrics were exposed to accelerated aging tests under varying temperature and time period in order to construct Neural Network models to predict weight loss and tensile strength loss percentages of the fabrics. The results of Artificial Neural Network models demonstrate that regression values are 0.98405 for weight loss percentages and 0.99935 for tensile strength loss percentages of the fabrics. Accordingly, the proposed Artificial Neural Network models are correctly constituted and the losses in determined fabric properties is successfully predicted.

Keywords

Aramid yarns, accelerated thermal aging, weight loss, tensile strength loss, artificial neural network

References

• 1. Khaliq Z, Zulifqar A. 2020. Textile Mechanics: Fibers and Yarns. In J. Hu, B. Kumar and J. Lu (Eds), Handbook of Fibrous Materials, Wiley, 435-455.
• 2. Vassiliadis S, Rangoussi M, Cay A, Provatidis C. 2010. Artifi cial Neural Networks and Their Applications in the Engineering of Fabrics. In Dobnik Dubrovski P (Ed), Woven Fabric Engineering. IntechOpen, 111-134.
• 3. Bhattacharjee D, Kothari V.K. 2007. A Neural Network System for Prediction of Thermal Resistance of Textile Fabrics. Textile Research Journal, 77:4-12.
• 4. Chattopadhyay R, Guha A. 2004. Artificial Neural Networks: Applications to Textiles. Textile Progress, 35:1-46.
• 5. Arıkan Kargı VS. 2014. A Comparison of Artificial Neural Networks and Multiple Linear Regression Models as In Predictors of Fabric Weft Defects. Tekstil ve Konfeksiyon, 24 (3), 309-316.
• 6. Tarafdar A, Shahi NC, Singh A, Sroh R. 2018. Artificial Neural Network Modeling of Water Activity: A Low Energy Approach to Freeze Drying. Food and Bioprocess Technology, 11:164-171.
• 7. Dolez PI, Tomer NS, Malajati Y. 2019. A quantitative method to compare the effect of thermal aging on the mechanical performance of fire protective fabrics. Journal of Applied Polymer Science, 136(1):47045.
• 8. Deng M, Tian M, Wang Y, Wang M. 2020. Quantitatively evaluating the effects of flash fire exposure on the mechanical performance of thermal protective clothing. International Journal of Clothing Science and Technology, 32 (3), 412-429.
• 9. Hoque MS, Saha A, Chung HJ, Dolez PI. 2022. Hydrothermal aging of fire‐protective fabrics. Journal of Applied Polymer Science, 139 (30), e52666.
• 10. Wang M, Li J. 2016. Thermal protection retention of fire protective clothing after repeated flash fire exposure. Journal of Industrial Textiles, 46(3), 737-755.
• 11. Aidani R, Dolez PI, Vu-khanh T. 2011. Effect of Thermal Aging on the Mechanical and Barrier Properties of an e-PTFE/NomexVR Moisture Membrane Used in Firefighters’ Protective Suits. Journal of Applied Polymer Science, 121:3101-3110.
• 12. Cho C, Nam SL, de la Mata AP, Harynuk JJ, Elias AL, Chung HJ, Dolez PI. 2022. Investigation of the accelerated thermal aging behavior of polyetherimide and lifetime prediction at elevated temperature. Journal of Applied Polymer Science, 139(15), 51955.
• 13. Liu X, Tian M, Wang Y, Su Y, Li J. 2021. Modeling to predict thermal aging for flame-retardant fabrics considering thermal stability under fire exposure. Textile Research Journal, 91:2656-2668.
• 14. Lemmi TS, Barburski M, Kabziński A, Frukacz K. 2021. Effect of Thermal Aging on the Mechanical Properties of High Tenacity Polyester Yarn. Materials, 14(7):1666-1676.
• 15. Arrieta C, David E, Dolez P, Toan V-KJ. 2010. Thermal Aging of a Blend of High-Performance Fibers, Journal of Applied Polymer Science, 115(5), 3031-3039.
• 16. Rezazadeh M, Torvi DA. 2011. Assessment of factors affecting the continuing performance of firefighters’ protective clothing: a literature review. Fire Technology, 47(3):565-599.
• 17. Lu Y, Wang L, Gao Q. 2018. Predicting tensile strength of fabrics used in firefighters’ protective clothing after multiple radiation exposures. Journal of The Textile Institute, 109:338-344.
• 18. Mandal S, Song G. 2014. An empirical analysis of thermal protective performance of fabrics used in protective clothing. Annals of Occupational Hygiene, 58(8):1065-1077.
• 19. Mandal S, Annaheim S, Greve J, Camenzind M, Rossi RM. 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.
• 20. Iyer RV, Sudhakar A, Vijayan K. 2006. Decomposition behavior of Kevlar 49 fibres: Part II. At T values < Td. High Perform. Polymers, 18:495-517.
• 21. Tian M, Wang Q, Xiao Y, Su Y, Zhang X, Li J. 2020. Investigating the thermal-protective performance of fire-retardant fabrics considering garment aperture structures exposed to flames. Materials, 13(16), 3579.
• 22. Brown, J R, and Browne, N M. Environmental effects on the mechanical properties of high-performance fibres. [PBI, Nomex, and Kevlar 49]. United States: N. p., 1976. Web.
• 23. Talukdar P, Das A, Alagirusamy R. 2016. Heat and mass transfer through thermal protective clothing–A review. International Journal of Thermal Sciences, 106, 32-56.
• 24. Serban A. 2019. The Impact of Heat Stress in Firefighter Fatalities. Honeywell Safety and Productivity Solutions. https://sps.honeywell. com/us/en/support/blog/safety/the-impact-of-heat-stress-in-firefighter-fatalities, Accessed 06 Jan 2023
• 25. Ozgen B, Pamuk G. 2014. Effects of thermal aging on Kevlar and Nomex fabrics. Industria Textila, 65(5):254-262.
• 26. International Standards Office. (2013). ISO 13934-1–Textiles-tensile properties of fabrics-part 1: Determination of maximum force and elongation at maximum force using the strip method: Bibliographical references: Electronic documents.
• 27. Eyupoglu C, Eyupoglu S, Merdan N. 2019. Improvement of thermal insulation properties of polyester nonwoven and estimation of thermal conductivity coefficients using artificial neural network. Journal of Testing and Evaluation, 47(2):20180129.
• 28. Veit, D. (Ed.). 2012. Simulation in textile technology: Theory and applications. Woodhead Publishing.
• 29. Omerogullari Basyigit, Z., Eyupoglu, C., Eyupoglu, S., & Merdan, N. 2023. Investigation and Feed‐Forward Neural Network‐Based Estimation of Dyeing Properties of Air Plasma Treated Wool Fabric Dyed with Natural Dye Obtained from Hibiscus Sabdariffa. Coloration Technology.
• 30. Eyupoglu, C., Eyupoglu, S., & Merdan, N. 2022. Investigation of dyeing properties of mohair fiber dyed with natural dyes obtained from candelariella reflexa. Journal of Natural Fibers, 19(16), 12829-12848.
• 31. Jain, A., & Vijayan, K. 2002. Thermally induced structural changes in Nomex fibres. Bulletin of Materials Science, 25, 341-346.
• 32. More JJ. 1978. The Levenberg-Marquardt algorithm: Implementation and Theory. In Watson GA (Ed.) Numerical Analysis. Berlin, Heidelberg: Springer; 105–116.
• 33. Eyupoglu C, Eyupoglu S, Merdan N. 2021. A multilayer perceptron artificial neural network model for estimation of ultraviolet protection properties of polyester microfiber fabric. Journal of The Textile Institute, 112:1403-1416.
• 34. Wackerly D, Mendenhall W, Scheaffer RL. 2008. Mathematical Statistics with Applications. Thomson Brooks/Cole, Belmont.