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Defect detection method based on sparse scanning with laser ultrasonics.

Chenwei Wang¹, Rui Han¹, Yihui Zhang¹

¹State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.

Scientific Reports

|April 16, 2025

Summary

This summary is machine-generated.

This study introduces a sparse scanning method for efficient on-line defect detection in metal additive manufacturing using laser ultrasonic technology. The method accurately identifies defect location and morphology with significantly reduced data requirements.

Keywords:

Additive manufacturing Defect detection Laser ultrasonics On-line detection Sparse scanning

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Area of Science:

Materials Science
Mechanical Engineering
Non-destructive Testing

Background:

Metal additive manufacturing (AM) is rapidly advancing, increasing the need for robust quality control of AM components.
On-line defect detection is crucial for ensuring the integrity and reliability of metal AM parts.
Laser ultrasonic technology offers potential for non-contact, in-situ monitoring during the AM process.

Purpose of the Study:

To develop and validate an efficient on-line defect detection method for metal AM using laser ultrasonic technology.
To improve detection efficiency and accuracy in characterizing defect position and morphology.
To investigate the interaction mechanisms between ultrasonic waves and defects for improved detection algorithms.

Main Methods:

A novel defect detection method based on sparse scanning of ultrasonic data was proposed.
The method utilizes the propagation paths of ultrasonic waves and scanning parameters to characterize defect edges.
Experimental validation involved five sets of experiments on four typical defect types using sparse scanning.

Main Results:

The method accurately determined the position and edge morphology of defects larger than 1 mm.
Compared to the Synthetic Aperture Focusing Technique (SAFT), the proposed method used 15.5% of the data and achieved 31.92% of SAFT's computational efficiency with a 27% Mean Absolute Error (MAE).
For a 0.4 mm internal hole defect, results consistent with SAFT were obtained using 32% of the data.

Conclusions:

The proposed sparse scanning defect detection method is efficient and suitable for on-line monitoring in metal additive manufacturing.
This approach significantly reduces data acquisition and processing requirements compared to traditional methods like SAFT.
The findings support the integration of this technology for enhanced quality control in AM processes.