Silicone sealant defect detection via 3D image reconstruction from multiple ultrasonic sensors

Wei-Yang Chung; Stefano Sfarra; Yuan Yao

doi:10.1117/12.3009840

9 May 2024 Silicone sealant defect detection via 3D image reconstruction from multiple ultrasonic sensors

Wei-Yang Chung, Stefano Sfarra, Yuan Yao

Proceedings Volume 12952, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE II; 1295203 (2024) https://doi.org/10.1117/12.3009840
Event: SPIE Smart Structures + Nondestructive Evaluation, 2024, Long Beach, California, United States

Abstract

In the production and packaging of silicone sealants, entrapped air, impurities, and foreign particles can introduce defects affecting performance. This study utilizes three ultrasonic non-destructive testing techniques: contact-based, angle-beam, and through-transmission testing, to identify defects and generate 3D images. The scanning process takes vertical A-scan measurements across the sample and rotates it at predetermined angles for comprehensive coverage. The contact-based technique uses the time-of-flight principle to determine defect locations, but struggles with defects aligned perpendicular and experiences signal reduction. The angle-beam method identifies defects in areas previously out of reach, but the slow sound movement in sealants can hinder capturing specific signals. While through-transmission offers enhanced signal clarity and an improved signal-to-noise ratio, pinpointing the defect’s exact depth is challenging. By combining these methods, the study reconstructs a more accurate three-dimensional image which visualizes the defective region.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wei-Yang Chung, Stefano Sfarra, and Yuan Yao "Silicone sealant defect detection via 3D image reconstruction from multiple ultrasonic sensors", Proc. SPIE 12952, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE II, 1295203 (9 May 2024); https://doi.org/10.1117/12.3009840

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