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Structural health monitoring (SHM) is a rapidly growing field focused on detecting damage in complex systems before catastrophic failure occurs. Advanced sensor technologies are necessary to fully harness SHM in applications involving harsh or remote environments, life-critical systems, mass-production vehicles, robotic systems, and others. Fiber Bragg Grating (FBG) sensors are attractive for in-situ health monitoring due to their resistance to electromagnetic noise, ability to be multiplexed, and accurate real-time operation. Ultrasonic additive manufacturing (UAM) has been demonstrated for solid-state fabrication of 3D structures with embedded FBG sensors. In this paper, UAM-embedded FBG sensors are investigated with a focus on SHM applications. FBG sensors embedded in an aluminum matrix 3 mm from the initiation site are shown to resolve a minimum crack length of 0.286 ± 0.033 mm and track crack growth until near failure. Accurate crack detection is also demonstrated from FBGs placed 6 mm and 9 mm from the crack initiation site. Regular acrylate-coated FBG sensors are shown to repeatably work at temperatures up to 300 ∘ C once embedded with the UAM process.
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This content will become publicly available on September 8, 2026
Wireless Ultrasonic Phased Arrays for On-Demand Wall Thickness Measurement and Damage Detection
Abstract Ultrasonics structural health monitoring (SHM) is widely recognized as an effective technique that enables early damage detection in large-scale structures and helps prevent potential catastrophic failures. Ultrasonic phased array technology has gained prominence in SHM due to its ability to inspect a large area with high spatial resolution. However, conventional systems often rely on physical wired sensor networks, limiting their deployment for hard-to-access regions. In this study, we present a wireless ultrasonic phased array system capable of dual-mode operation for both wall thickness measurement and structural damage detection. The system integrates wireless power transfer (WPT) modules and customized matching circuits, enabling efficient and flexible deployment. Proof-of-concept experiments demonstrate successful wall thickness evaluation and accurate defect localization in metallic structures using both delay-and-sum (DAS) and minimum variance (MV) imaging methods, with the MV algorithm offering improved imaging resolution. Future work will focus on advancing real-time monitoring through machine learning, enabling 3D imaging, and extending system applicability to anisotropic composite materials.
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- PAR ID:
- 10657173
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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