Title: Micromechanics-Integrated Artificial Neural Networks Model for the Prediction of Stress-Strain Response of Carbon Nanotube-Reinforced Nanocomposites

Authors: Kil Taegeon, Bae Jin-Ho, Yang Beomjoo, H.K. Lee

DOI: 10.33599/nasampe/c.22.0007

Abstract: Various types of nanoscale fibers have been incorporated into the polymer matrix to improve the mechanical properties of the nanocomposites [1]. Carbon nanotubes (CNTs) are a potential candidate for nanocomposites owing to their low density and excellent mechanical properties [2]. However, due to the inherent properties of CNTs (e.g., their interfacial characteristics and aggregation), accurate prediction of the mechanical behavior of CNT-reinforced nanocomposites is difficult [3]. Hence, this prompted us to propose a micromechanics-integrated artificial neural network (ANN) model to predict the stress–strain responses of CNT-reinforced nanocomposites. Tensile tests of CNT-reinforced nanocomposites are performed to obtain the training data, and three input features (i.e., the volume fraction of CNTs, strain, and strain energy which is calculated based on micromechanics) are considered in the ANN model [4]. A comparison of the predictions with the experimental results verifies the applicability of the ANN model. A good agreement is revealed between the ANN model predictions and experimental results, with an R-squared value of 0.98 and a mean absolute error of 0.72. REFERENCE [1] Cha J, Jun GH, Park JK, Kim JC, Ryu HJ, Hong SH. Improvement of modulus, strength and fracture toughness of CNT/Epoxy nanocomposites through the functionalization of carbon nanotubes. Compos Part B Eng 2017;129:169–79. [2] Treacy MMJ, Ebbesen TW, Gibson JM. Exceptionally high Young’s modulus observed for individual carbon nanotubes. Nature 1996;381:678–80. [3] Yang S, Yu S, Ryu J, Cho JM, Kyoung W, Han DS, et al. Nonlinear multiscale modeling approach to characterize elastoplastic behavior of CNT/polymer nanocomposites considering the interphase and interfacial imperfection. Int J Plast 2013;41:124–46. [4] Kazi MK, Eljack F, Mahdi E. Predictive ANN models for varying filler content for cotton fiber/PVC composites based on experimental load displacement curves. Compos Struct 2020;254:112885.

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Conference: CAMX 2022

Publication Date: 2022/10/17

SKU: TP22-0000000007

Pages: 12

Price: $24.00