# Application of Machine Learning Models in Predicting Vibration Frequencies of Thin Variable Thickness Plates

## TL;DR

This paper shows how machine learning can efficiently predict the vibration frequencies of thin plates with varying thickness, reducing the need for costly simulations.

## Contribution

The study introduces a tuned artificial neural network as a computationally efficient surrogate model for predicting plate vibration frequencies.

## Key findings

- ANNs accurately predicted eigenvalues with significantly less computational effort than finite element analysis.
- Systematic tuning of ANN architecture and hyperparameters improved prediction accuracy and reduced overfitting.
- Data preprocessing steps enhanced model stability and performance.

## Abstract

This study investigates the application of machine learning (ML) techniques for predicting vibration frequencies of thin rectangular plates with variable thickness. Traditional optimization methods, such as genetic algorithms, require repeated solutions of the plate vibration eigenproblem using finite element (FE) analysis, which is computationally expensive. To reduce this cost, a surrogate model based on artificial neural networks (ANNs) is proposed as an efficient alternative. The dataset includes variations in plate geometry, boundary conditions, and thickness distribution, encoded numerically for model training. ANN architecture and hyperparameters—such as the number of hidden layers, neurons per layer, and activation functions—were systematically tuned to achieve high prediction accuracy while avoiding overfitting. Data preprocessing steps, including standardization and scaling, were applied to improve model stability. Performance was evaluated using metrics such as RMSE and R2. The results demonstrate that ANNs can accurately predict eigenvalues with significantly reduced computational effort compared to FE analysis. This approach offers a practical solution for integrating machine learning into structural optimization workflows.