Delamination Detection in CFRP Components from Ultrasound Images Using Convolutional Neural Networks

被引：0

作者：

Seesselberg, Tilman ^{[1
]}

Busboom, Axel ^{[1
]}

Welsch, Jonas ^{[2
]}

Cretu, Edmond ^{[2
]}

Rohling, Robert ^{[2
]}

机构：

[1] Munich Univ Appl Sci, Dept Engn & Management, Munich, Germany

[2] Univ British Columbia, Elect & Comp Engn, Vancouver, BC, Canada

来源：

2024 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE, I2MTC 2024 | 2024年

关键词：

carbon fibre-reinforced polymers (CFRP); convolutional neural networks (CNN); image classification; non-destructive testing (NDT); ultrasound imaging;

D O I：

10.1109/I2MTC60896.2024.10561030

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Carbon fibre-reinforced polymer (CFRP) materials are used for various applications due to their desirable mechanical properties. Defects, such as delaminations, can occur during production or operation of CFRP components and can compromise their structural integrity. Ultrasonic imaging is a well-established and cost-effective technique for non-destructive testing (NDT) of CFRP components. We evaluate the use of supervised training of artificial neural networks (ANNs) for the automated detection of delaminations from ultrasound B-scans. After hyperparameter optimization, convolutional neural networks (CNNs) performed better than multilayer perceptrons (MLPs). Using a test specimen, we achieved a recall of 100% and a precision of 94.3% on unseen test images.

引用

页数：6

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