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

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Abstract

In this study, the sound absorption performance of warp-knitted spacer fabrics (WKSFs) was investigated. WKSFs with different angles of connecting yarns between two surfaces and different numbers of layers were fabricated. The effect of porosity and the number of layers on the sound absorption coefficient (SAC) of WKSFs were measured by the impedance tube method at the frequency range of 0–6100 Hz. The results indicated that increasing the angle of connecting yarns and the number of layers will enhance the SAC of WKSFs. An artificial neural network (ANN) model was also used to predict the effect of knit structure and the number of layers on the SAC of WKSFs at different frequencies. It is found that the ANN model provided an accurate and reliable prediction of SAC for different WKSF structures with a high value of correlation coefficient (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 fibrous acoustic materials.

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Authors and Affiliations

  1. Department of Textile Engineering, Amirkabir University of Technology, P.O. Box 1591634311, Tehran, Iran

    Mohammad Davoudabadi Farahani & Ali Asghar Asgharian Jeddi

  2. Department of Textile Engineering, Yazd University, P.O. Box 89195-741, Yazd, Iran

    Mohammad Davoudabadi Farahani & Mahdi Hasanzadeh

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  1. Mohammad Davoudabadi Farahani
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  2. Ali Asghar Asgharian Jeddi
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  3. Mahdi Hasanzadeh
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Correspondence to Mahdi Hasanzadeh.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Farahani, M.D., Jeddi, A.A.A. & Hasanzadeh, M. Predicting the Sound Absorption Performance of Warp-Knitted Spacer Fabrics via an Artificial Neural Network System. Fibers Polym 24, 1491–1501 (2023). https://doi.org/10.1007/s12221-023-00151-6

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