Automated Quality Characterization for Composites Using Hybrid Ultrasonic Imaging Techniques

Jiangtao Sun, Alvin Yung Boon Chong, Siamak Tavakoli, Guojin Feng, Jamil Kanfoud, Cem Selcuk, Tat Hean Gan

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


An enhanced technique using image processing has been developed for automated ultrasonic inspection of composite materials, such as glass/carbon-fibre-reinforced polymer (GFRP or CFRP), to ascertain their structural healthiness. The proposed technique is capable of identifying the abnormality features buried in the composite by image filtering and segmentation applied to ultrasonic C-Scan images. This work presents results performed on two composite samples with simulated delamination defects. A local gating scheme is applied to raw A-Scan data for improved contrast between defective and healthy regions in the produced C-Scan image. In this test campaign, different filtering and thresholding algorithms are evaluated and compared in terms of their effectiveness on defect identification. The accuracies of less than 3 mm and 1.11 mm were attained for the defect size and depth, respectively. The results demonstrates the applicability of the proposed technique for accurate defect localization and characterization of composite materials.

Original languageEnglish
Pages (from-to)205-230
Number of pages26
JournalResearch in Nondestructive Evaluation
Issue number4
Early online date12 Apr 2018
Publication statusPublished - 4 Jul 2019
Externally publishedYes


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