Investigation of Fiber Content and Porosity Effects on Tensile Strength in Long-Fiber-Reinforced Thermoplastics Using Artificial Neural Network 


Vol. 24,  No. 4, pp. 1389-1400, Apr.  2023
10.1007/s12221-023-00049-3


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  Abstract

The volume contents of composite materials directly aff ect the tensile strength of long-fi ber-reinforced thermoplastics (LFTs). However, it is not easy to analyze how factors such as the fi ber content and porosity aff ect the tensile strength of LFTs. With this motivation, we investigate the relationship between fi ber content, porosity, and tensile strength in various LFTs using a neural network (NN) approach. In this study, polyamide 6 (PA6) and polyphenylene sulfi de (PPS) are selected as the resin matrices, and glass fi ber (GF) and carbon fi ber (CF) are chosen as the reinforced fi bers. Therefore, the LFTs invoked in this work were PA6/GF, PA6/CF, PPS/GF, and PPS/CF. The proposed NN, which can predict the tensile strength of the utilized LFTs, was trained using the experimentally measured fi ber content, porosity, and tensile strength. Based on the learned NN, we then investigated the eff ect of fi ber content and porosity on the tensile strength in each LFT case. As a result, the proposed NN can continuously express the tensile strengths of LFTs in the given ranges of the fi ber content and porosity. It should be noted that the tendency of the tensile strength derived by the suggested NN matches well with the studied properties of LFTs. Consequently, through the proposed NN, it is possible to precisely analyze the tensile strengths of invoked LFTs while containing the trends of the LFTs. The detailed strategies for the experiments and NN approach are presented, and the performance of the proposed NN is evaluated through mathematical approaches and previously studied information on LFTs.

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  Cite this article

[IEEE Style]

J. Ahn, S. Kim, 3, S. Ahn, K. Kim, H. Yang, "Investigation of Fiber Content and Porosity Effects on Tensile Strength in Long-Fiber-Reinforced Thermoplastics Using Artificial Neural Network," Fibers and Polymers, vol. 24, no. 4, pp. 1389-1400, 2023. DOI: 10.1007/s12221-023-00049-3.

[ACM Style]

Jun-Geol Ahn, Sung-Eun Kim, 3, Seungjae Ahn, Ki-Young Kim, and Hyun-Ik Yang. 2023. Investigation of Fiber Content and Porosity Effects on Tensile Strength in Long-Fiber-Reinforced Thermoplastics Using Artificial Neural Network. Fibers and Polymers, 24, 4, (2023), 1389-1400. DOI: 10.1007/s12221-023-00049-3.

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pISSN : 1229 - 9197
eISSN : 1875 - 0052

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