量化和预测用油辣木树皮纤维增强的硅树脂的拉伸性能、

Mohd Nor Azmi Ab Patar, N. A. S. Manssor, Mohd Rashdan Isa, Nur Auni Izzati Jusoh, M. J. Abd Latif, P. N. Sivasankaran, Jamaluddin Mahmud
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引用次数: 0

摘要

为了更好地了解如何使用辣木树皮(MOB)作为有机硅基体的增强材料,本研究旨在确定这种新材料在单轴拉伸下的机械性能。研究人员制作了 MOB 粉含量分别为 0 wt%、4 wt%、8 wt%、12 wt% 和 16 wt% 的复合样品。使用新胡克安超弹性模型对拉伸性能进行了数学量化。收集到的数据被用于建立人工神经网络(ANN)的多个输入,通过 MATLAB 预测其材料常数。结果表明,纤维含量为 16 wt% 的样品的材料常数比纯硅胶高 63.9%。拉伸模量测试也证明了这一点,测试结果表明,随着纤维含量的增加,模量也随之增加。不过,与纯硅胶相比,MOB-硅胶生物复合材料的伸长率(λ)略有下降。最后,使用 ANN 对材料常数进行预测的百分比误差为 2.03%,这表明它与数学模型相当。因此,在有机硅中加入 MOB 纤维可产生更硬的材料,并逐步改善复合材料。此外,具有多重输入(加权、载荷和伸长率)的网络在进行精确预测方面更为可靠。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantifying and predicting the tensile properties of silicone reinforced with Moringa oleifera bark fibers,
To obtain a better understanding of using Moringa oleifera bark (MOB) as a reinforcement in a silicone matrix, this study aimed to define the mechanical properties of this new material under uniaxial tension. Composite samples of 0 wt%, 4 wt%, 8 wt%, 12 wt%, and 16 wt% MOB powder were produced. The tensile properties were quantified mathematically using the neo-Hookean hyperelastic model. The collected data were employed to establish multiple inputs of an artificial neural network (ANN) to predict its material constant via MATLAB. The result showed that the material constant for the 16 wt% fiber content sample was 63.9% higher than pure silicone. This was supported by the tensile modulus testing, which indicated that the modulus increased as the fiber content increased. However, the elongation ratio (λ) of the MOB-silicone biocomposite decreased slightly compared to the pure silicone. Lastly, the prediction of the material constant using an ANN recorded a 2.03% percentage error, which showed that it was comparable to the mathematical modelling. Therefore, the inclusion of MOB fibers into silicone produced a stiffer material and gradually improved the composite. Furthermore, the network that had multiple inputs (weighting, load, and elongation) was more reliable to produce precise predictions.
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