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A Multi-Layer Multi-Pass Weld Bead Cross-Section Morphology Extraction Method Based on Row-Column Grayscale

Ting Lei^1,2, Shixiang Gong^1,2, Chaoqun Wu^1,2

¹School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan 430070, China.

Materials (Basel, Switzerland)

|October 16, 2024

Summary

This summary is machine-generated.

This study introduces an automated method for extracting weld bead cross-section morphology, improving consistency and efficiency in welding quality analysis. The new technique accurately identifies key features, aiding in better weld assessments.

Keywords:

feature extraction method image identification multi-layer multi-pass weld bead morphology

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Area of Science:

Materials Science and Engineering
Non-Destructive Testing
Image Processing

Background:

Weld bead cross-section morphology is vital for assessing welding quality.
Manual extraction methods are inconsistent and inefficient.
Automated extraction is needed for reliable quality control.

Purpose of the Study:

To develop an automated method for extracting multi-layer, multi-pass weld bead cross-section morphology.
To improve the consistency and efficiency of weld quality analysis.
To provide a basis for detailed analysis and improved welding quality assessment.

Main Methods:

Image pre-processing of weld bead cross-section images.
Row-column grayscale segmentation based on average gray values.
Extraction of weld contour using a binarization threshold (ESI).
Image fusion and morphological processing for feature extraction.
Calculation of weld feature parameters like circumference and area.

Main Results:

Relative errors for circumference and area within 10%.
Maximum weld seam width and height discrepancies close to true values.
High image quality assessment with average SSIM > 0.9 and average PSNR > 60.
Feasible extraction of general contour features for multi-layer, multi-pass welds.

Conclusions:

The proposed automated method effectively extracts weld bead cross-section morphology.
The technique offers improved accuracy and efficiency over manual methods.
This provides a foundation for enhanced welding quality assessment and control.