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A Multi-Objective Optimization Method for Cylindrical Surface Ultrasonic Array Parameters Based on BPNN and NSGA-II.

Xin Zeng^1,2,3, Xueshen Cao^2,3, Jiaheng Zhao^2,3

¹University of Chinese Academy of Sciences, Beijing 100049, China.

Sensors (Basel, Switzerland)

|November 13, 2025

Summary

This study introduces a multi-objective optimization strategy for cylindrical ultrasonic transducers to enhance detection resolution. The method uses the NSGA-II algorithm to optimize geometric parameters, improving performance for engineering applications.

Keywords:

BPNN NSGA-II algorithm cylindrical ultrasonic array multi-objective optimization

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Area of Science:

Engineering
Materials Science
Acoustics

Background:

Ultrasonic array design parameters critically influence detection performance metrics like resolution.
Transducer structural design affects side lobe levels, beam directivity, and focal spot size.

Purpose of the Study:

To propose a multi-objective optimization strategy for cylindrical surface ultrasonic transducers.
To improve the selection of design parameters for enhanced resolution and performance.

Main Methods:

A nonlinear mapping was established between array geometric parameters and resolution performance variables.
The NSGA-II algorithm was used for optimization on a trained BPNN (Backpropagation Neural Network).
Optimization objectives included main-lobe width, side-lobe intensity, and sound-pressure uniformity.

Main Results:

A Pareto-optimal solution set was generated, providing optimized geometric configurations.
Acoustic field simulations validated the derived configurations.
Dynamic aperture regularity was observed under millimeter-level resolution requirements.

Conclusions:

The proposed optimization strategy effectively enhances the geometric design of cylindrical ultrasonic arrays.
Findings provide valuable insights for real-world engineering applications.
The study demonstrates a systematic approach to optimizing transducer design for improved performance.