Classification and Prediction of Vibration Natural Frequencies of a Circular Plate Using Chladni Patterns and Deep Learning Techniques

In this research work the resonant frequencies and the corresponding mode shapes of circular Chladni plate is used to analyze the vibration response of the plate. A camera captures Chladni patterns for training, classification, and prediction of respective frequencies using deep learning algorithm. The Chladni patterns formed, depend on the material properties, geometry, thickness, and the vibration frequency. The experimental setup consists of a circular Chladni plate of acrylic with 300 mm diameter and 2.48 mm thickness. The simulation of mode shapes software ANSYS (student’s version) is used. Comparison of the Chladni plate experimental results and ANSYS simulation shows error of < 14%. Deep learning techniques shows the validation accuracy with pretrained network VGG16 as 99.16% and 99.49% with GoogleNet and prediction accuracies 98.63% with GoogleNet and 97.98% with VGG16. The resonant frequencies along with its mode shapes can be predicted using the Chladni plate approach combined with deep learning.