Artificial Intelligence prediction and optimization of the mechanical strength of modified Natural Fibre/MWCNT polymer nanocomposite

IF 6.7 3区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Patrick Ehi Imoisili , Mamookho Elizabeth Makhatha , Tien-Chien Jen
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Abstract

Artificial Intelligence (AI), techniques like artificial neural networks (ANN), and machine learning have been used to solve a variety of engineering problems. In this study, Multiwall Carbon Nanotube (MWCNT) and Natural Fibres (NF) from plantain (Musa Paradisiaca) fiber (PF), were utilized to prepare a reinforced hybrid polymer nanocomposite for advanced composite applications. A chemical solution containing potassium permanganate (KMnO4) in acetone (C3H6O) was applied to modify the fibers to alter their surface and improve adhesion and interaction between the PF/polymer matrix. To predict and optimize the tensile strength (TS) of the prepared PF/MWCNT hybrid nanocomposite (PFMNC), the ANN model with hyper-parameter optimization in a single-layer-perceptron architecture of 3-5-1 was used with 5 neurons in the hidden layer, and Box-Behnken Design (BBD) was utilized. Scanning electron microscope (SEM) micrographs demonstrate that KMnO4 modification has impacted the TS of the hybridized nanocomposite. Mechanical Test results show that these variables impacted the TS of the PFMNC as shown by analysis of variance (ANOVA) with R2 = 0.9986. The expected findings were nearly identical to the experimental results. The model predicted an optimal tensile strength of 46.1563 Mpa. To substantiate the reliability of the empirical experimental investigation, TS analysis was performed at predicted optimal settings. TS results showed an average strength of 45.4401 Mpa. About 98.45 % of the projected tensile strength is accounted for by the model. The present study has demonstrated the effectiveness of the ANN-BBD modeling technique in achieving the appropriate mechanical property values quickly, reducing production costs, and preserving resources.

人工智能预测和优化改性天然纤维/MWCNT 聚合物纳米复合材料的机械强度
人工智能(AI)、人工神经网络(ANN)和机器学习等技术已被用于解决各种工程问题。在这项研究中,利用多壁碳纳米管(MWCNT)和来自车前草纤维(PF)的天然纤维(NF)制备了一种增强型杂化聚合物纳米复合材料,用于先进的复合材料应用。使用丙酮(CHO)中含有高锰酸钾(KMnO)的化学溶液对纤维进行改性,以改变其表面,改善 PF/聚合物基体之间的粘附性和相互作用。为了预测和优化所制备的 PF/MWCNT 杂化纳米复合材料(PFMNC)的拉伸强度(TS),采用了单层感知器结构的超参数优化 ANN 模型(3-5-1)和盒式贝肯设计(BBD)。扫描电子显微镜(SEM)显微照片显示,KMnO 改性影响了杂化纳米复合材料的 TS。机械测试结果表明,方差分析(ANOVA)显示这些变量对 PFMNC 的 TS 有影响,R = 0.9986。预期结果与实验结果几乎相同。模型预测的最佳拉伸强度为 46.1563 兆帕。为了证实经验实验调查的可靠性,在预测的最佳设置下进行了 TS 分析。TS 结果显示平均强度为 45.4401 兆帕。预计拉伸强度的约 98.45% 由模型计算得出。本研究证明了 ANN-BBD 建模技术在快速获得适当的机械性能值、降低生产成本和保护资源方面的有效性。
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来源期刊
Journal of Science: Advanced Materials and Devices
Journal of Science: Advanced Materials and Devices Materials Science-Electronic, Optical and Magnetic Materials
CiteScore
11.90
自引率
2.50%
发文量
88
审稿时长
47 days
期刊介绍: In 1985, the Journal of Science was founded as a platform for publishing national and international research papers across various disciplines, including natural sciences, technology, social sciences, and humanities. Over the years, the journal has experienced remarkable growth in terms of quality, size, and scope. Today, it encompasses a diverse range of publications dedicated to academic research. Considering the rapid expansion of materials science, we are pleased to introduce the Journal of Science: Advanced Materials and Devices. This new addition to our journal series offers researchers an exciting opportunity to publish their work on all aspects of materials science and technology within the esteemed Journal of Science. With this development, we aim to revolutionize the way research in materials science is expressed and organized, further strengthening our commitment to promoting outstanding research across various scientific and technological fields.
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