Related Experiment Video
Updated: Jun 14, 2025
11:34
Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
Published on: May 15, 2017
11.1K
Laser ultrasonic detection for defects of LAM components based on variable time window intensity mapping with
Zhuangzhuang Wan1, Xue Bai1, Jian Ma1
1Laser Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250104, China.
Photoacoustics
|September 2, 2024
Summary
A new laser ultrasonic system effectively detects surface defects in metal laser additive manufacturing (LAM). This method uses adaptive denoising and imaging to identify flaws as small as 0.5 mm, crucial for quality control.
Area of Science:
- Materials Science
- Non-destructive Testing
- Additive Manufacturing
Background:
- Metallurgical defects are common in metal laser additive manufacturing (LAM) due to complex thermodynamic processes.
- Effective detection of surface and near-surface defects is critical for ensuring the quality and reliability of LAM parts.
- Existing detection methods may struggle with the inherent surface roughness of LAM components.
Purpose of the Study:
- To design and validate a laser ultrasonic system for detecting surface/near-surface defects in the layer-by-layer LAM process.
- To develop an advanced ultrasonic imaging approach for defect characterization.
- To establish a reliable method for online defect monitoring in additive manufacturing.
Main Methods:
- A laser ultrasonic system was designed for pulsed laser irradiation and ultrasonic wave detection.
- Ultrasonic imaging was performed using variable time window intensity mapping.
- Adaptive 2σ threshold denoising, based on Gaussian mixture models and expectation-maximization, was employed for noise reduction and defect differentiation.
- The system was tested on LAM samples with varying surface roughness.
Main Results:
- The laser ultrasonic system successfully detected surface and near-surface defects.
- Ultrasonic wave reflection at defect boundaries was used to characterize defect size and location.
- The adaptive denoising method effectively differentiated defects from background noise, accommodating surface roughness up to 37.5 μm.
- Defects as small as 0.5 mm in diameter and depth were detectable.
Conclusions:
- The proposed laser ultrasonic system and imaging approach provide a viable solution for online detection of metallurgical defects in metal laser additive manufacturing.
- The adaptive denoising technique enhances detection accuracy and reliability, even on rough surfaces.
- This method holds significant potential for improving quality control and reducing failures in additive manufacturing processes.
