机器学习辅助下亚麻纤维/形状记忆环氧湿化复合材料的拉伸模量预测

IF 12.2 1区 工程技术 Q1 MECHANICS
T. Sadat
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引用次数: 0

摘要

亚麻纤维/形状记忆型环氧吸湿复合材料是生物复合材料领域的研究热点。本文重点研究了这些复合材料的拉伸模量,并研究了纤维取向(0°和90°),温度(20°C, 40°C, 60°C, 80°C和100°C)和湿度(50%和完全浸入)条件等因素对其的影响。基于非线性相关初始变量,利用机器学习算法预测拉伸模量。采用决策树(DT)和随机森林(RF)算法对数据进行分析,结果显示,决定系数R2分别为0.94和0.95。这些发现证明了机器学习在分析生物复合材料力学性能的大型数据集方面的有效性。此外,研究表明,亚麻纤维的取向对拉伸模量值的影响最大(DT和RF模型的特征重要度分别为0.598和0.605),这表明这是设计这些材料时需要考虑的关键因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning-Assisted Tensile Modulus Prediction for Flax Fiber/Shape Memory Epoxy Hygromorph Composites
Flax fiber/shape memory epoxy hygromorph composites are a promising area of research in the field of biocomposites. This paper focuses on the tensile modulus of these composites and investigates how it is affected by factors such as fiber orientation (0° and 90°), temperature (20 °C, 40 °C, 60 °C, 80 °C, and 100 °C), and humidity (50% and fully immersed) conditions. Machine learning algorithms were utilized to predict the tensile modulus based on non-linearly dependent initial variables. Both decision tree (DT) and random forest (RF) algorithms were employed to analyze the data, and the results showed high coefficient of determination R2 values of 0.94 and 0.95, respectively. These findings demonstrate the effectiveness of machine learning in analyzing large datasets of mechanical properties in biocomposites. Moreover, the study revealed that the orientation of the flax fibers had the greatest impact on the tensile modulus value (with feature importance of 0.598 and 0.605 for the DT and RF models, respectively), indicating that it is a crucial factor to consider when designing these materials.
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来源期刊
CiteScore
28.20
自引率
0.70%
发文量
13
审稿时长
>12 weeks
期刊介绍: Applied Mechanics Reviews (AMR) is an international review journal that serves as a premier venue for dissemination of material across all subdisciplines of applied mechanics and engineering science, including fluid and solid mechanics, heat transfer, dynamics and vibration, and applications.AMR provides an archival repository for state-of-the-art and retrospective survey articles and reviews of research areas and curricular developments. The journal invites commentary on research and education policy in different countries. The journal also invites original tutorial and educational material in applied mechanics targeting non-specialist audiences, including undergraduate and K-12 students.
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