Artificial intelligence schemes to predict the mechanical performance of lignocellulosic fibers with unseen data to enhance the reliability of biocomposites

IF 1.5 4区 工程技术 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Rami Al-Jarrah, Faris M. AL-Oqla
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引用次数: 0

Abstract

Purpose

This work introduces an integrated artificial intelligence schemes to enhance accurately predicting the mechanical properties of cellulosic fibers towards boosting their reliability for more sustainable industries.

Design/methodology/approach

Fuzzy clustering and stacked method approach were utilized to predict the mechanical performance of the fibers. A reference dataset contains comprehensive information regarding mechanical behavior of the lignocellulosic fibers was compiled from previous experimental investigations on mechanical properties for eight different fiber materials. Data encompass three key factors: Density of 0.9–1.6 g/cm3, Diameter of 5.9–1,000 µm, and Microfibrillar angle of 2–49 deg were utilized. Initially, fuzzy clustering technique was utilized for the data. For validating proposed model, ultimate tensile strength and elongation at break were predicted and then examined against unseen new data that had not been used during model development.

Findings

The output results demonstrated remarkably accurate and highly acceptable predictions results. The error analysis for the proposed method was discussed by using statistical criteria. The stacked model proved to be effective in significantly reducing level of uncertainty in predicting the mechanical properties, thereby enhancing model’s reliability and precision. The study demonstrates the robustness and efficacy of the stacked method in accurately estimating mechanical properties of lignocellulosic fibers, making it a valuable tool for material scientists and engineers in various applications.

Originality/value

Cellulosic fibers are essential for biomaterials to enhance developing green sustainable bio-products. However, such fibers have diverse characteristics according to their types, chemical composition and structure causing inconsistent mechanical performance. This work introduces an integrated artificial intelligence schemes to enhance accurately predicting the mechanical properties of cellulosic fibers towards boosting their reliability for more sustainable industries. Fuzzy clustering and stacked method approach were utilized to predict the mechanical performance of the fibers.

利用未见数据预测木质纤维素纤维机械性能的人工智能方案,提高生物复合材料的可靠性
设计/方法/方法利用模糊聚类和堆叠法预测纤维的机械性能。参考数据集包含有关木质纤维素纤维机械性能的全面信息,该数据集是从以前对八种不同纤维材料的机械性能进行的实验研究中整理出来的。数据包含三个关键因素:密度为 0.9-1.6 g/cm3,直径为 5.9-1,000 µm,微纤维角度为 2-49 deg。最初,对数据采用了模糊聚类技术。为了验证所提出的模型,对极限拉伸强度和断裂伸长率进行了预测,然后根据模型开发过程中未使用过的新数据进行了检验。使用统计标准讨论了所提方法的误差分析。事实证明,叠加模型能有效降低机械性能预测的不确定性,从而提高模型的可靠性和精确度。该研究证明了堆叠法在准确估算木质纤维素纤维机械性能方面的稳健性和有效性,使其成为材料科学家和工程师在各种应用中的宝贵工具。然而,此类纤维因其类型、化学成分和结构而具有不同的特性,导致其机械性能不一致。这项工作介绍了一种综合人工智能方案,以提高对纤维素纤维机械性能的准确预测,从而增强纤维素纤维的可靠性,促进工业的可持续发展。模糊聚类和堆叠法被用来预测纤维的机械性能。
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来源期刊
Engineering Computations
Engineering Computations 工程技术-工程:综合
CiteScore
3.40
自引率
6.20%
发文量
61
审稿时长
5 months
期刊介绍: The journal presents its readers with broad coverage across all branches of engineering and science of the latest development and application of new solution algorithms, innovative numerical methods and/or solution techniques directed at the utilization of computational methods in engineering analysis, engineering design and practice. For more information visit: http://www.emeraldgrouppublishing.com/ec.htm
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