Improving Depth Resolution of Ultrasonic Phased Array Imaging to Inspect Aerospace Composite Structures

Area of Science:

• Materials Science
• Aerospace Engineering
• Signal Processing

Background:

• Composite materials are crucial in aerospace but require advanced non-destructive evaluation (NDE) methods.
• Ultrasonic phased array (PA) technology offers potential for autonomous NDE, but signal processing challenges exist, especially at higher frequencies.
• Structural noise from layer boundaries complicates defect detection in composite materials.

Purpose of the Study:

• To present challenges and achievements in developing a compact ultrasonic PA module for autonomous NDE of aerospace composites.
• To propose and validate a novel signal processing algorithm for enhanced defect detection and characterization.
• To address signal processing challenges, specifically structural noise, encountered with 10 MHz PA transducers.

Main Methods:

• Analysis of ultrasonic scan data from 5 MHz and 10 MHz PA transducers.
• Development of a wavelet transform-based algorithm for defect detection and characterization.
• Implementation of a smart thresholding technique using statistical noise parameters (mean and standard deviation).

Main Results:

• The proposed wavelet transform algorithm successfully detects and characterizes defects, providing 3D depth, size, and shape information.
• The algorithm effectively mitigates structural noise, a common issue in higher frequency PA imaging of composites.
• Validation against a standard calibration specimen confirmed the algorithm's accuracy in defect depth determination.

Conclusions:

• The developed compact ultrasonic PA module with advanced signal processing shows promise for autonomous NDE of aerospace composites.
• The wavelet transform-based algorithm offers a robust solution for overcoming structural noise and accurately characterizing defects.
• This approach enhances the reliability and efficiency of inspecting critical composite aerospace structures.