Research on the Acoustic Emission Source Localization Methodology in Composite Materials based on Artificial Intelligence

TL;DR

This paper presents AESLNet, an AI-based methodology for localizing acoustic emission sources in composite materials, achieving high accuracy through wavelet-transformed signals and optimized neural network regression.

Contribution

Introduction of AESLNet, a convolutional neural network with Bayesian hyper-parameter optimization, for precise acoustic emission source localization in anisotropic composite materials.

Findings

Average localization error of 3.02mm

Resolution of 20mm in source detection

Effective use of wavelet scalograms as training data

Abstract

In this study, methodology of acoustic emission source localization in composite materials based on artificial intelligence was presented. Carbon fiber reinforced plastic was selected for specimen, and acoustic emission signal were measured using piezoelectric devices. The measured signal was wavelet-transformed to obtain scalograms, which were used as training data for the artificial intelligence model. AESLNet(acoustic emission source localization network), proposed in this study, was constructed convolutional layers in parallel due to anisotropy of the composited materials. It is regression model to detect the coordinates of acoustic emission source location. Hyper-parameter of network has been optimized by Bayesian optimization. It has been confirmed that network can detect location of acoustic emission source with an average error of 3.02mm and a resolution of 20mm.