Ultrasonic Multi-Resolution Imaging for Micro-Defect Quantification
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Solution Overview
Problem
Traditional ultrasonic testing methods struggle to simultaneously achieve high lateral resolution and detection depth for micro-defects, especially those smaller than the probe's focal column diameter, leading to reduced signal-to-noise ratio and limited detection capability, particularly in high-performance parts like 3D printed nickel-based superalloys.
Innovation Solution
A quantitative detection method using low-frequency ultrasonic multi-resolution scanning imaging, involving calibration, fast Fourier transform, band-pass filtering, and split spectrum processing to decompose signals into sub-signals with different center frequencies, enabling multi-resolution scanning images and directivity function analysis to accurately quantify defect sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-frequency ultrasonic probes are used to improve lateral resolution, then lateral resolution is improved, but detection depth is reduced and signal-to-noise ratio deteriorates
Solution Approach 1:
The patent divides the ultrasonic signal spectrum into multiple frequency bands through spectral decomposition. By segmenting the signal processing into different frequency components, the system can analyze defects at multiple resolution levels simultaneously, achieving high lateral resolution for small defects while maintaining detection depth through lower frequency components.
Solution Approach 2:
The patent transforms the single-frequency ultrasonic detection into multi-frequency spectral analysis. By adding the frequency dimension to the traditional time-domain signal processing, the system can resolve defects of different sizes at different depth levels, effectively resolving the contradiction between lateral resolution and detection depth.
2Measurement precision
If probe aperture is increased to improve lateral resolution, then lateral resolution is improved, but device complexity and cost increase
Solution Approach 1:
The patent changes the frequency parameter of the ultrasonic signal rather than physically modifying the probe aperture. By using spectral decomposition to create virtual high-resolution images from low-frequency signals, the system achieves improved lateral resolution without increasing probe complexity or cost.
3Measurement precision
If focal length is reduced to improve lateral resolution, then lateral resolution is improved, but detection depth range is limited
Solution Approach 1:
The patent segments the detection process into multiple frequency bands, each providing different resolution characteristics. Low-frequency components provide deep penetration while high-frequency components provide fine resolution, allowing the system to maintain both wide detection depth range and high lateral resolution simultaneously through multi-resolution image fusion.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method enhances signal-to-noise ratio, detection depth, and lateral resolution, allowing accurate quantification of micro-defects down to hundreds of microns, overcoming limitations of high-frequency focusing probes and phased array ultrasonic imaging.
Implementation Method 1
collecting ultrasonic A-type echo signals
Implementation Method 2
low-frequency ultrasonic probe
Implementation Method 3
Performing fast Fourier transform on the acquired echo signals x(t) to obtain an amplitude spectrum A(f) thereof
Implementation Method 4
Performing band-pass filtering with the center frequency fi on the signals x(t) using split spectrum processing to decompose the signals x(t) into N sub-signals yi(t) with different center frequencies fi
Data Source
AI summary
In a quantitative detection method of micro-defects through low-frequency ultrasonic multi-resolution scanning imaging, the collected ultrasonic A-type scanning signals are decomposed through split spectrum processing into sub-signals with different center frequencies fi, amplitude imaging is performed on the same sub-signals fi, a defect size di detected by a half of the amplitude of the image is identified, linear fitting is performed on di with fi according to the sound field directivity function, and di corresponding to the slope k=−0.01 of the fitting curve is the quantitative size of the defect.


