Abstract
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.
Highlights
Structural health monitoring (SHM) provides improved safety and decreased costs through real-time sensor monitoring of engineering systems
SHM has the potential to reduce the need for non-destructive inspection (NDI) which typically requires the system to be out of commission while costly, labor intensive testing is performed [1]
We investigate the use of Ultrasonic additive manufacturing (UAM)-embedded Fiber Bragg Grating (FBG) sensors in SHM prognostic applications
Summary
Structural health monitoring (SHM) provides improved safety and decreased costs through real-time sensor monitoring of engineering systems. SHM has been incorporated in a wide range of applications including civil, aerospace, and industrial systems, there is a need to develop more capable system monitoring and diagnostic tools. SHM has the potential to reduce the need for non-destructive inspection (NDI) which typically requires the system to be out of commission while costly, labor intensive testing is performed [1]. SHM systems could allow continuous and safer operations where defects are detected in real time. Research is being conducted on the use of artificial intelligence, including neural networks, to improve the ability of sensors to detect defects in SHM applications [2]
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