Prediction and Analysis of Acoustic Displacement Field Using the Method of Neural Network

Micro/nano manipulation technology holds significant value in diverse fields, such as biomedicine, precision killing, and material chemistry. Among these applications, acoustic manipulation technology stands out as a crucial approach for micro/nano manipulation. However, the determination of a control equation for acoustic manipulation proves challenging due to the complex motion characteristics exhibited by micro/nano targets. The mastery of acoustic displacement field information is essential for successful manipulation of micro targets. Addressing this challenge, this paper presents a novel method that utilizes neural networks for predicting sound wave displacement fields. Which involves collecting raw data on acoustic displacement changes under specific excitation frequencies and times utilizing the acoustic manipulation experimental platform. Using the data to train the neural network model. The network takes the initial position information of the micro target as input and accurately predicts the displacement field across the entire thin plate area, achieving a remarkable prediction accuracy of 0.5mm. Through a comprehensive analysis of the network’s prediction performance using test set data, the effectiveness of the designed network in solving the acoustic displacement field problem with high accuracy is demonstrated. This research significantly contributes to the advancement of acoustic manipulation technology, enabling precise control and manipulation of micro/nano targets across various applications.