Predicting the Sound Absorption Performance of Warp-Knitted Spacer Fabrics via an Artificial Neural Network System 


Vol. 24,  No. 4, pp. 1491-1502, Apr.  2023
10.1007/s12221-023-00151-6


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  Abstract

In this study, the sound absorption performance of warp-knitted spacer fabrics (WKSFs) was investigated. WKSFs with diff erent angles of connecting yarns between two surfaces and diff erent numbers of layers were fabricated. The eff ect of porosity and the number of layers on the sound absorption coeffi cient (SAC) of WKSFs were measured by the impedance tube method at the frequency range of 06100 Hz. The results indicated that increasing the angle of connecting yarns and the number of layers will enhance the SAC of WKSFs. An artifi cial neural network (ANN) model was also used to predict the eff ect of knit structure and the number of layers on the SAC of WKSFs at diff erent frequencies. It is found that the ANN model provided an accurate and reliable prediction of SAC for diff erent WKSF structures with a high value of correlation coeffi cient (more than 0.99%). The obtained results showed that the developed high-precision ANN model would be a helpful and powerful tool for modeling and predicting sound absorption performance of fi brous acoustic materials.

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  Cite this article

[IEEE Style]

M. D. Farahani, A. A. A. Jeddi, M. Hasanzadeh, "Predicting the Sound Absorption Performance of Warp-Knitted Spacer Fabrics via an Artificial Neural Network System," Fibers and Polymers, vol. 24, no. 4, pp. 1491-1502, 2023. DOI: 10.1007/s12221-023-00151-6.

[ACM Style]

Mohammad Davoudabadi Farahani, Ali Asghar Asgharian Jeddi, and Mahdi Hasanzadeh. 2023. Predicting the Sound Absorption Performance of Warp-Knitted Spacer Fabrics via an Artificial Neural Network System. Fibers and Polymers, 24, 4, (2023), 1491-1502. DOI: 10.1007/s12221-023-00151-6.

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