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机器学习模型的应用,用于预测薄变厚板的振动频率

Łukasz Domagalski¹, Izabela Kowalczyk¹

¹Department of Structural Mechanics, Lodz University of Technology, Politechniki 6, 93-590 Lodz, Poland.

Materials (Basel, Switzerland)

|January 10, 2026

概括

机器学习,特别是人工神经网络 (ANN),准确地预测了薄板振动频率. 与传统的有限元分析方法相比,这种方法显著降低了计算成本.

科学领域:

计算力学计算力学计算力学
机器学习应用机器学习应用
结构动力学结构动力学

背景情况:

预测厚度可变的薄矩形板的振动频率对于结构分析至关重要.
传统的方法,如有限元 (FE) 分析,由于重复的自身问题解决方案,因此在计算上昂贵.
在优化中使用的遗传算法加剧了计算负担.

研究的目的:

研究机器学习 (ML) 技术的应用,用于预测薄矩形板的振动频率.
使用人工神经网络 (ANN) 开发一个高效的代孕模型,以降低计算成本.
为了提高效率,将ML集成到结构优化工作流程中.

主要方法:

创建了一个数据集,其中包含板状几何,边界条件和厚度分布的变化,编码为数值.
人工神经网络 (ANN) 被用作替代模型.
进行了ANN架构和超参数 (隐藏层,神经元,激活函数) 的系统调整和数据预处理 (标准化,缩放).

主要成果:

在板振动固有值方面,ANN表现出高的预测准确度.
与FE分析相比,ML方法显著降低了计算工作量.
根平均平方误差 (RMSE) 和R平方 (R2) 度量证实了模型的性能.

结论:

人工神经网络为预测薄矩形板的振动频率提供了一个准确且计算效率高的替代方案.
这种基于机器学习的替代建模方法为优化结构分析工作流提供了切实可行的解决方案.
该研究强调了将机器学习集成到结构工程中的潜力,以减少计算成本.

关键词:

机器学习是机器学习.自然频率的自然频率.代孕模型的代孕模型变化厚度的板块可以变化.这些都是振动,振动,振动.

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