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首页> 外文期刊>International Journal of Mechanical Sciences >Development of a prediction method of Rayleigh damping coefficients for free layer damping coatings through machine learning algorithms
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Development of a prediction method of Rayleigh damping coefficients for free layer damping coatings through machine learning algorithms

机译:通过机器学习算法的自由层阻尼涂层瑞利阻尼系数预测方法的研制

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摘要

Application of damping coatings on metal sheets is a commonly used method to suppress the undesirable vibration and noise levels in various industries. As numerical simulations have a vital role while designing a high-quality product with fewer costs, an accurate and practical way of modelling such type of structures is necessary. It was aimed to develop a methodology that helps to define damping parameters of such viscoelastic coating layers through Rayleigh damping coefficients. Machine learning tools were considered to find a prediction formula which yields Rayleigh coefficients based on thicknesses. For this purpose, several tests were conducted with different coating thicknesses on steel plates. In parallel, a great number of simulations were performed not only to make comparisons with the reference values from tests but also to feed the learning algorithms with the data sets. The results were compared including the ones from the Oberst equation. The results from the machine learning showed significantly better matching performance with the tests, as there seems to be a limitation problem for Oberst accuracy.
机译:阻尼涂层对金属板的应用是一种常用的方法,可以抑制各个行业中不希望的振动和噪声水平。由于数值模拟具有重要作用,同时设计具有较少成本的高质量产品,需要一种准确和实用的建模这种结构的方式。它旨在开发一种方法,有助于通过瑞利阻尼系数限定这种粘弹性涂层的阻尼参数。考虑机器学习工具寻找预测公式,其基于厚度产生瑞利系数。为此目的,在钢板上用不同的涂层厚度进行几种测试。同时,不仅执行大量的模拟,不仅可以与来自测试的参考值进行比较,而且还执行与数据集的学习算法进行比较。比较结果,包括来自Oberst方程的结果。机器学习的结果显示出明显更好的匹配性能,因为令人熟练的准确性似乎有一个限制问题。

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