Machine-Learning-Based Real-Time Photoacoustic Surface Crack Detection

Photoacoustic imaging is commonly utilized in biomedical research due to its capability to provide the functional and structural details of imaging targets, featuring optical contrast and ultrasound resolution. This imaging technique has also found applications in industry, particularly in non-destr...

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Bibliographic Details
Published in:Engineering proceedings 2023-10, Vol.56 (1), p.92
Main Authors: Abdulrhman Alshaya, Ghadah Alabduljabbar, Asem Alalwan
Format: Article
Language:English
Subjects:
crack
industry
machine learning
non-destructive testing
photoacoustic
ultrasound
Online Access:Get full text
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Summary:Photoacoustic imaging is commonly utilized in biomedical research due to its capability to provide the functional and structural details of imaging targets, featuring optical contrast and ultrasound resolution. This imaging technique has also found applications in industry, particularly in non-destructive testing, such as in surface crack detection. However, the cost of photoacoustic systems and the time required for scanning and image reconstruction limit their use in non-destructive testing. In this study, low-cost photoacoustic equipment was combined with machine learning techniques and applied in surface crack detection. This scanning technique achieved a 97% offline prediction accuracy. Additionally, it demonstrated a reduction in system complexity compared to traditional photoacoustic imaging techniques. This reduction in complexity results from using a single scanning line as input to the machine learning model, unlike the imaging technique, which requires multiple scanning lines to reconstruct the photoacoustic image.
ISSN:2673-4591
DOI:10.3390/ASEC2023-15328