Machine learning prediction models for investigating vibration properties of epoxy resin under moisture conditions

IF 2.8 3区 工程技术 Q2 MECHANICS International Journal of Non-Linear Mechanics Pub Date : 2024-07-26 DOI:10.1016/j.ijnonlinmec.2024.104857
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Abstract

Epoxy resins used in engineering applications are commonly exposed to wet environment during intended service life, which causes vibration property degradation and increasing risk of structural failure. In this work, vibration properties of epoxy resin plate under different moisture conditions are predicted with various sizes and boundary conditions using developed machine learning (ML) models. The dataset of epoxy vibration is established first, where values in the dataset are calculated with five moisture contents using previously developed meshless model. The dataset from meshless simulation is used to train ML models of epoxy vibration using six different algorithms, including support vector machine, decision tree, random forest, gradient boosting decision tree, extreme gradient boosting, and artificial neural network. It is found that the prediction model developed using extreme gradient boosting algorithm shows the highest accuracy of 99.9% and strong reliability. Using this model, vibration properties of epoxy resin with a series of sizes and boundary conditions are predicted under various moisture contents from dry case to saturated case, which deepens the understanding of the effects of wet environments on the vibration responses of epoxy resins. The results could be used for analysis of durability of epoxy resin, and the developed ML prediction models contribute to investigating vibration property of epoxy resin under different moisture conditions, which is crucial for ensuring durability of epoxy resin in wet environment.

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用于研究环氧树脂在潮湿条件下振动特性的机器学习预测模型
工程应用中使用的环氧树脂在预期使用寿命内通常会暴露在潮湿的环境中,从而导致振动性能下降,增加结构失效的风险。在这项工作中,利用开发的机器学习(ML)模型,预测了环氧树脂板在不同湿度条件下的振动特性,包括各种尺寸和边界条件。首先建立环氧树脂振动数据集,利用之前开发的无网格模型计算数据集中五种水分含量的值。利用无网格模拟的数据集,使用六种不同的算法训练环氧树脂振动的 ML 模型,包括支持向量机、决策树、随机森林、梯度提升决策树、极端梯度提升和人工神经网络。结果发现,使用极梯度提升算法建立的预测模型准确率最高,达到 99.9%,可靠性强。利用该模型,可以预测一系列尺寸和边界条件的环氧树脂在从干燥到饱和的不同含水量条件下的振动特性,加深了人们对潮湿环境对环氧树脂振动响应影响的理解。这些结果可用于分析环氧树脂的耐久性,所开发的 ML 预测模型有助于研究环氧树脂在不同湿度条件下的振动特性,这对于确保环氧树脂在潮湿环境中的耐久性至关重要。
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来源期刊
CiteScore
5.50
自引率
9.40%
发文量
192
审稿时长
67 days
期刊介绍: The International Journal of Non-Linear Mechanics provides a specific medium for dissemination of high-quality research results in the various areas of theoretical, applied, and experimental mechanics of solids, fluids, structures, and systems where the phenomena are inherently non-linear. The journal brings together original results in non-linear problems in elasticity, plasticity, dynamics, vibrations, wave-propagation, rheology, fluid-structure interaction systems, stability, biomechanics, micro- and nano-structures, materials, metamaterials, and in other diverse areas. Papers may be analytical, computational or experimental in nature. Treatments of non-linear differential equations wherein solutions and properties of solutions are emphasized but physical aspects are not adequately relevant, will not be considered for possible publication. Both deterministic and stochastic approaches are fostered. Contributions pertaining to both established and emerging fields are encouraged.
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