Automated defect detection for ultrasonic inspection of CFRP aircraft components-Reference-Cited by-同舟云学术

Automated defect detection for ultrasonic inspection of CFRP aircraft components

Published:2021-09 Issue: Volume:122 Page:102478
ISSN:0963-8695
Container-title:NDT & E International
language:en
Short-container-title:NDT & E International

Author:

Séguin-Charbonneau Loïc^ORCID,Walter Julien^ORCID,Théroux Louis-Daniel,Scheed Laurent^ORCID,Beausoleil Alexandre^ORCID,Masson Benoit^ORCID

Funder

Natural Sciences and Engineering Research Council of Canada

Fonds de recherche du Québec – Nature et technologies

Publisher

Elsevier BV

Subject

Mechanical Engineering,Condensed Matter Physics,General Materials Science

Reference17 articles.

1. American Society for Nondestructive Testing., ASNT industry handbook : aerospace NDT;Bossi,2014

2. Progress on the development of automated data analysis algorithms and software for ultrasonic inspection of composites;Aldrin;AIP Conf Proc,2014

3. Flatbed scanner image and single ultrasonic rangefinder technique for composite laminates defect detection;Mahmod;2013 IEEE Int Conf on Smart Instrum, Meas and Appl (ICSIMA),2013

4. Automatic processing of ultrasound images for nondestructive testing;Goodfriend,1993

Cited by 36 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Comprehensive evaluation of CFRP laminates using NDT methods for aircraft applications;Journal of Materials Research and Technology;2024-09

2. Advancing Carbon Fiber Composite Inspection: Deep Learning-Enabled Defect Localization and Sizing via 3-D U-Net Segmentation of Ultrasonic Data;IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control;2024-09

3. A fiber optic laser-ultrasonic transmitter based on collapsed photonic crystal fiber for ultrasonic testing;Structural Health Monitoring;2024-08-13

4. Enhancing precision of defect 3D reconstruction in metal ultrasonic testing through point cloud completion;Ultrasonics;2024-08

5. Structural health monitoring in aviation: a comprehensive review and future directions for machine learning;Nondestructive Testing and Evaluation;2024-05-22