Effective Defect Detection Using Instance Segmentation for NDI

Conference · Fri Jan 24 04:00:00 EST 2025

OSTI ID:2563178

Rahman, Ashiqur ^[1]; Reddy Seethi, Venkata Devesh ^[2]; Yunker, Austin ^[3]; Kral, Zachary ^[4]; Kettimuthu, Rajkumar ^[3]; Alhoori, Hamed ^[2]

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1. Northern Illinois Univ., DeKalb, IL (United States); Spirit AeroSystems Inc
2. Northern Illinois Univ., DeKalb, IL (United States)
3. Argonne National Laboratory (ANL), Argonne, IL (United States)
4. Spirit AeroSystems, Inc., Wichita, KS (United States)

Ultrasonic testing is a common Non-Destructive Inspection (NDI) method used in aerospace manufacturing. However, the complexity and size of the ultrasonic scans make it challenging to identify defects through visual inspection or machine learning models. Using computer vision techniques to identify defects from ultrasonic scans is an evolving research area. In this study, we used instance segmentation to identify the presence of defects in the ultrasonic scan images of composite panels that are representative of real components manufactured in aerospace. We used two models based on Mask- RCNN (Detectron 2) and YOLO 11 respectively. Additionally, we implemented a simple statistical pre-processing technique that reduces the burden of requiring custom-tailored pre-processing techniques. Our study demonstrates the feasibility and effectiveness of using instance segmentation in the NDI pipeline by significantly reducing data pre-processing time, inspection time, and overall costs.

• Research Organization: Spirit AeroSystems, Inc., Wichita, KS (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• DOE Contract Number: EE0009397
• OSTI ID: 2563178
• Country of Publication: United States
• Language: English

Related Subjects

Non-Destructive Inspection (NDI)
computer vision techniques
ultrasonic testing