Laser ultrasound wave pattern analysis for efficient defect detection in samples with curved surfaces

Area of Science:

• Materials Science
• Non-Destructive Testing
• Acoustics

Background:

• Curved sample surfaces in production (e.g., welding, additive manufacturing) challenge traditional quality inspection methods.
• Existing characterization techniques are typically optimized for flat geometries, limiting their effectiveness on curved samples.

Purpose of the Study:

• To present a laser ultrasound (LUS) method for efficient defect detection in finite samples with curved surfaces.
• To reduce scanning effort and eliminate the need for prior defect location knowledge.

Main Methods:

• Generalized simulations of LUS wave patterns in samples with varying curvature, width, and excitation parameters (thermoelastic/ablative).
• Analysis of wave patterns to predict optimal excitation points for weld coverage.
• Simulation analysis using B-scans to identify optimal excitation-detection pairs for defect localization.
• Comparison of simulation predictions with experimental results.

Main Results:

• Wave pattern analysis successfully predicted optimal excitation spots for reaching all points in a weld.
• Simulation identified favorable excitation-detection pairs for detecting defects anywhere in the weld seam.
• Experimental validation showed an almost threefold increase in defect detectability using predicted optimal positions.

Conclusions:

• The developed LUS method significantly enhances defect detection reliability and time efficiency for curved surfaces.
• This approach is expected to improve quality inspection in industrial applications involving curved components.