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

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.