A hybrid machine learning and particle swarm system for configuring holes on cantilever beams to achieve desired natural frequencies

In this paper, a hybrid machine learning/optimization system is developed to identify the optimal configuration of holes on a cantilever beam to achieve a desired natural frequency. Based on a design of experiments, ۱۰۰ configurations are selected from the vast possible combinations of placing five holes on a ۵x۲۱ matrix grid over the beam. The natural frequencies for these configurations are obtained using frequency analysis in COMSOL. A dataset containing the hole configurations and their corresponding normalized first natural frequency is constructed to build a machine-learning model using the LightGBM method. The particle swarm optimization algorithm is employed to find the optimal hole configuration that yields the desired natural frequency. The results demonstrate the success of the developed hybrid system, as the machine learning model accurately predicts both the training and testing data. Additionally, the optimization algorithm successfully identifies hole configurations that closely match the desired natural frequency in various test cases, validating the system's effectiveness.

کلیدواژه ها:

frequency tuning ، Machine Learning ، hole configuration ، Frequency analysis ، frequency adjustment