Automatic Segmentation of Gas Metal Arc Welding for Cleaner Productions
Abstract
:Featured Application
Abstract
1. Introduction
- A publicly available real-world GMAW welding dataset for quality assessment
- Human segmentation and human knowledge labeling of the welding images in the dataset, including some of the most common errors.
- Evaluation of automatic segmentation by traditional and deep neural network-based models
2. Related Works
- Safety: Defective welds can lead to severe structural failures in products or infrastructure, posing a significant risk to human safety. Detecting these defects early can prevent accidents and human loss.
- Efficiency: Inspecting welds manually is a process that can be slow, expensive, and prone to human error. Automatic systems can speed up inspection and allow more quality tests to be performed in less time, improving productivity and reducing manufacturing costs.
- Quality and Consistency: Weld quality can vary depending on factors such as welder technique, environmental conditions, and material quality. Automatic detection methods can provide a more accurate and consistent weld quality assessment compared to traditional visual inspections.
- Cost Reduction: Automatic detection can reduce costs associated with repairing defective welds and labor costs for inspection. It also improves traceability and quality management throughout the manufacturing process.
- Defect Complexity: Welds can have very complex defects that are not always visible to the naked eye, and their exact location can be challenging to determine. Detecting them using automatic technologies, such as ultrasound, X-ray, or computer vision, requires advanced approaches to ensure that potential flaws are not overlooked.
3. Materials and Methods
3.1. Dataset of Images
3.2. Hardware and Software Used
- Fanuc Robot M710iC/50.
- Lincoln Electric Welding Gun
- Wire feeder, AutoDrive 4R90 Lincoln Electric
- Weld Wire.
- Clamps
- Security device
- Fanuc Robot Controller, R30iA
- Power control, Power Wave i400 Lincoln Electric.
3.3. Automatic Segmentation Methodology
4. Results and Discussion
4.1. Comparison with Manual Segmentation
4.2. Assessment of Segmentation Methods
4.2.1. Visual Comparison
4.2.2. Quantitative Comparison
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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C | Mn | P | S | Cu | Ni | Cr | Mo | V | Cb | |
---|---|---|---|---|---|---|---|---|---|---|
CS Type A | 0.100 | 0.600 | 0.030 | 0.035 | 0.200 | 0.200 | 0.150 | 0.060 | 0.008 | 0.008 |
Yield Strength ksi | Elongation in 2 in [50 mm]% | |
---|---|---|
CS Type A | 30 to 50 | ≥25 |
Parameter | Value |
---|---|
Wire feed | 145.000 |
Trim | 1.050 |
Voltage | 29.000 |
Travel speed | 36.000 |
Pulse | 0.045 |
Air mix | Ar + CO2 |
Feedback Current | 199.400 |
Category | Example of Weld Seam | Criteria |
---|---|---|
Good | Homogeneous welding, without any porosity or black shadow that warns of a perforation or splash when welding | |
Robot | The weld appears homogeneous but shows spatter in small spheres around the weld bead. Part of the side of the unwelded piece can be seen, so the robot can pass through it again, correcting the path. | |
Human | The weld bead is not homogeneous. It can be thin in some parts, bulge in the middle, and have a lot of spatter. However, the color is clear, meaning it was not overheated. Therefore, the weld can be removed by a person with a chisel and hammer so that the weld bead can be made again. | |
Scrap | Round shadows appear on the weld bead, so there is a risk of overheating and possible perforation of the part, which would not support a second application of welding. |
Segmentation Method | Thresholding | ROI |
---|---|---|
Binary Segmentation | ||
Binary Segmentation + OTSU Method | ||
OTSU Method | ||
Triangular segmentation | ||
Rembg | - |
Category | Binary + Contours | Rembg |
---|---|---|
Good | 24.86% | 85.89% |
Human | 26.03% | 85.77% |
Robot | 25.23% | 85.59% |
Scrap | 24.08% | 87.71% |
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Davila-Iniesta, E.M.; López-Islas, J.A.; Villuendas-Rey, Y.; Camacho-Nieto, O. Automatic Segmentation of Gas Metal Arc Welding for Cleaner Productions. Appl. Sci. 2025, 15, 3280. https://doi.org/10.3390/app15063280
Davila-Iniesta EM, López-Islas JA, Villuendas-Rey Y, Camacho-Nieto O. Automatic Segmentation of Gas Metal Arc Welding for Cleaner Productions. Applied Sciences. 2025; 15(6):3280. https://doi.org/10.3390/app15063280
Chicago/Turabian StyleDavila-Iniesta, Erwin M., José A. López-Islas, Yenny Villuendas-Rey, and Oscar Camacho-Nieto. 2025. "Automatic Segmentation of Gas Metal Arc Welding for Cleaner Productions" Applied Sciences 15, no. 6: 3280. https://doi.org/10.3390/app15063280
APA StyleDavila-Iniesta, E. M., López-Islas, J. A., Villuendas-Rey, Y., & Camacho-Nieto, O. (2025). Automatic Segmentation of Gas Metal Arc Welding for Cleaner Productions. Applied Sciences, 15(6), 3280. https://doi.org/10.3390/app15063280