THE HAGUE, 26th September 2024.
In modern manufacturing, non-destructive testing (NDT) is a critical process for ensuring product integrity without compromising the material or part. The more prevalent testing method is Ultrasonic Testing, but lesser-known Acoustic Resonance Testing (ART) offers its own unique advantages. While both techniques provide accurate defect detection, they differ in approach, application, and efficiency. This article compares UT and ART, highlighting how AI-driven acoustic resonant testing offers unique benefits for automated, high-volume production lines.
What is Ultrasonic Testing (UT)?
Ultrasonic testing (UT) is a well-established NDT method that employs high-frequency sound waves, typically ranging between 0.5 MHz and 15 MHz, to inspect the internal structure of materials. In this process, a piezoelectric transducer generates sound waves that propagate through the material. When these waves encounter a discontinuity, such as a crack or void, a portion of the wave energy reflects back to the transducer, creating an "echo."
The time-of-flight (TOF) of the echo is used to determine the location and size of the defect. The amplitude of the returned signal provides additional information about the flaw’s characteristics, such as depth and shape. This method is highly precise and can detect sub-surface defects in metals, composites, and other solid materials.
Advantages of Ultrasonic Testing:
Suitable for small volumes.
Can detect exact location and size of a defect.
Suitable for large parts such as airplane wings and ships.
Limitations:
Requires highly skilled operators for the interpretation of data.
Surface conditions, such as roughness or complex geometries, are not compatible with UT
Coupling materials (e.g., gel) are often required to transmit sound waves effectively.
What is Acoustic Resonance Testing (ART) and How Does AI Enhance Its Capabilities?
Acoustic Resonance Testing (ART) is an advanced NDT technique that exploits the natural resonance frequencies of a component to assess its structural integrity. Every solid object has a set of unique resonance frequencies that are determined by its material properties, geometry, and internal composition. ART involves exciting the component—typically by mechanical impact, vibration, or air-coupled transducers—and measuring its vibrational response. Furthermore, ART operates effectively with complex geometrical shapes thanks to the wider dispersion of lower frequencies.
The vibrational spectrum of the test object is then compared to a reference spectrum of a “known good” part. Deviations in resonance patterns, such as shifts in frequency or amplitude, indicate potential defects. These can include cracks, inclusions, porosity, voids, and even subtle material inconsistencies such as variations in density, stiffness, or composition.
Advantages of Acoustic Resonance Testing:
Suitable for a larger variety of defects
Detects changes in material characteristics such as stress or hardness
Works well with complex geometries and surfaces
Limitations
Requires a skilled operator to analyze the frequency patterns
Requires “known good” reference parts for comparison
Does not indicate the exact location of defect
Automating ART with RESONIKS’ AI takes this process a step further by integrating machine learning algorithms into the testing workflow. Our system indexes the resonant signatures of defect-free parts during the training phase. The AI model analyzes these data points and creates a highly sensitive profile that can detect even minute anomalies in newly produced parts. This results in a fully automated, objective, and repeatable testing process with minimal human intervention. With RESONIKS AI-powered defect detection, all the previous advantages of ART still apply, but with added benefits due to machine learning.
Advantages of RESONIKS AI-Driven ART:
Ideal for large production volume
High-testing speed increases throughput
Fully automated – no skilled operator needed
Fast and objective testing with no previous know-how necessary
Detect defects earlier in production process
Suitable for noisy production environments
Limitations:
Each part requires a training phase to familiarize our AI with your production
Does not indicate the exact location of defect
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