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$Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction$

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Grain Scattering Noise Modeling and Its Use in the Detection and Characterization of Defects Using Ultrasonic Arrays.

Long Bai, Alexander Velichko, Bruce W Drinkwater

IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

|July 9, 2019

Summary

Grain scattering in ultrasonic array imaging for non-destructive testing (NDT) hinders defect detection. This study introduces a defect+grains model to statistically analyze grain structures, improving defect characterization and predicting inspection performance limits.

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Area of Science:

Materials Science
Non-Destructive Testing (NDT)
Ultrasonic Imaging

Background:

Material structural noise from grain scattering is a primary error source in ultrasonic array imaging for NDT.
Polycrystalline material grains impede defect detection and characterization due to scattering, attenuation, and backscatter.

Purpose of the Study:

To propose a novel defect+grains model for improved defect detection and characterization in ultrasonic NDT.
To provide a statistical framework for understanding the impact of grain structures on defect detection accuracy.

Main Methods:

Developing a statistical model that incorporates multiple realizations of grain structures with potential defects.
Calculating probabilities by matching experimental measurements to the proposed defect+grains models.

Main Results:

The defect+grains model statistically captures essential information for defect detection and characterization.
Detection, classification, and sizing accuracy can be predicted by quantifying model matching probabilities.

Conclusions:

The proposed defect+grains modeling approach offers insights into NDT challenges.
This method allows for the evaluation of fundamental limits in achievable ultrasonic inspection performance.