Ultrasound Waveguide Sensor Network for Differentiating Sensitization and Fatigue in Aluminum Alloys

The Advanced Sensor Technology Laboratory (ASTL) recently embarked on a groundbreaking project to develop ultrasound waveguide (USWG) sensor networks. This initiative aims to differentiate microstructural damage, such as sensitization and fatigue, in aluminum alloys. Inspired by the discovery that a vibrating bar can function as a Fabry-Perot interferometer (FPI), this project bridges the gap between optical fiber sensors and ultrasound domain, expanding concepts like FPI and Fiber Brag grating (FBG) to USWG sensors. These USWG sensors offer significant advantages over traditional optical fiber sensors.

  1. They allow for physics-based and quantitative data interpretation thanks to a known analytical model.
  2. The structure being inspected is an integral part of the USWG sensors, meaning its material properties directly influence the sensor’s resonances.
  3. They enable time-frequency analysis in software, simplifying the acquisition and interpretation of data, making the process more cost-effective and flexible.

The project involves creating a network of these sensors by bonding USWG to the structure at multiple locations. Two types of USWG sensors will be implemented: the Ultrasound Fabry-Perot Interferometric (UFPI) and the Ultrasound Bragg Grating (UBG). The UFPI is formed by bonding a section of USWG to the structure with homogenous adhesive, creating a FPI cavity, while the UBG uses patterned adhesive to create constructive interference and a distinct reflectance spectrum.

The project’s objectives include quantifying the effects of sensitization and fatigue on structural and adhesive material properties, developing methodologies for creating and validating UFPI and UBG sensors, and exploring networking strategies and machine learning for data interpretation. This research is anticipated to lay a solid foundation for USWG sensor networks, potentially revolutionizing various research fields and applications, much like OF sensor networks.