Detection of Hydrogen Bubbles Produced by Corrosion Inhibition of Metal Weldment Using Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Weldment Preparation
2.2. Electrochemical Corrosion Testing
2.3. YOLOv4
3. Results and Discussion
3.1. Weldment Structures and Hardness
3.2. Potentiodynamic Polarization
3.3. Observation of Bubbles Detection and H2 Evolution
- Dataset Preparation: Gather 3755 images of the bubble dataset with annotated images and the corresponding bounding boxes around the objects of interest, along with the class labels.
- Configuration: Adjust the hyperparameters in the configuration files. Modify the settings, such as the batch size, learning rate, and input image size (Table 2).
- Training Execution: Execute the training process using the prepared dataset. The model learns to detect objects by minimizing detection errors through optimization.
- Loss Calculation: Compute the loss functions (localization, confidence, and classification) based on the differences between the predicted and true bounding boxes, objectness scores, and class predictions.
- Backpropagation and Optimization: Backpropagate the calculated loss to optimize the model’s parameters using optimization techniques, like stochastic gradient descent (SGD) or its variants (Figure 9a).
- Model Evaluation: Periodically assess the model’s performance on a validation dataset using metrics, such as precision, recall, and mAP (mean average precision) (Figure 9b).
- Fine-tuning and Iteration: Fine-tune the model by adjusting the hyperparameters or training it on additional data if needed and iterating until the desired accuracy is achieved.
- Model Deployment: Deploy the trained model for inference on new data or integrate it into applications for real-time object detection tasks (Figure 10).
3.4. SEM and FTIR Observation
3.5. Proposed Mechanism of Inhibition
4. Conclusions
- With varying efficacy for each weldment zone, sodium molybdate boosts the weldment’s corrosion resistance in 1 M HCl solution. The oxide layer’s stability is increased by the sodium molybdate’s mixed-type inhibition, which also increases the weldment’s corrosion resistance.
- The weld metal’s maximal inhibitory efficiency of the SM solution reached 59% with 0.4 g/L, the heat-affected zone at 52% with 0.2 g/L, and the base metal at 37% with 0.2 g/L of extract.
- The results of training the deep learning model using 3755 bubble images on the mAP50 reached 97.11%, showing that the minimum average bubble detected for the WM was 0.353 /mm2 at an SM concentration of 0.4 g/L, while the HAZ was 0.612 /mm2 at 0.2 g /L, and the BM was 1.055 /mm2 at 0.2 g/L.
- The results from the three conducted tests—potentiodynamic polarization, hydrogen collection examinations, and bubble detection examinations (using deep learning)—display a consistent pattern regarding corrosion behavior. This suggests the potential application of deep learning techniques to analyze corrosion behavior in different scenarios.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameters | Values |
---|---|
Current Type | DC |
Polarity | DCEP (Direct Current Electrode Positive) |
Current (A) | 90 |
Electrode Type | E6013 |
Arc Voltage (V) | 24 |
Travel Speed (mm/min) | ±150 |
Electrode Angle (degrees) | ±45 |
Arc Length (mm) | 3–8 |
Type of Butt Welds | Double-V butt joints with single pass each side |
Groove Angle (degrees) | 60 |
Butt Gap (mm) | 1 |
Parameters | Values | Parameters | Values |
---|---|---|---|
Batch | 64 | Angle | 0 |
Subdivision | 32 | Learning rate | 0.001 |
Width | 352 | Burn_in | 1000 |
Height | 352 | Max_batches | 6000 |
Channels | 3 | Policy | Steps |
Momentum | 0.949 | Steps | 4800; 5400 |
Decay | 0.0005 | Scales | 0.1:0.1 |
Inhibitor Concentration (g/L) | βa (V/dec) | −βc (V/dec) | Ecorr (V) | icorr (×10−4 A/cm2) | Corrosion Rate (mm/Year) | IE (%) |
---|---|---|---|---|---|---|
WM | ||||||
0 | 0.122 ± 0.02 | 0.145 ± 0.03 | −0.495 ± 0.01 | 4.28 ± 0.92 | 17.78 ± 0.89 | 0 |
0.1 | 0.130 ± 0.02 | 0.272 ± 0.02 | −0.488 ± 0.01 | 2.72 ± 0.81 | 11.30 ± 0.57 | 36.46 |
0.2 | 0.124 ± 0.02 | 0.137 ± 0.03 | −0.479 ± 0.01 | 2.08 ± 0.87 | 8.63 ± 0.43 | 51.45 |
0.3 | 0.124 ± 0.02 | 0.146 ± 0.03 | −0.483 ± 0.01 | 2.08 ± 1.02 | 8.66 ± 0.44 | 51.29 |
0.4 | 0.110 ± 0.02 | 0.117± 0.03 | −0.465 ± 0.01 | 1.74 ± 0.32 | 7.21 ± 0.36 | 59.28 |
HAZ | ||||||
0 | 0.085 ± 0.03 | 0.095 ± 0.03 | −0.493 ± 0.01 | 1.09 ± 0.05 | 4.54 ± 0.23 | 0 |
0.1 | 0.053 ± 0.03 | 0.054 ± 0.03 | −0.497 ± 0.01 | 7.29 ± 0.36 | 3.03 ± 0.15 | 33.32 |
0.2 | 0.044 ± 0.03 | 0.043 ± 0.03 | −0.495 ± 0.01 | 5.20 ± 0.26 | 2.16 ± 0.11 | 52.38 |
0.3 | 0.067 ± 0.03 | 0.065 ± 0.03 | −0.488 ± 0.01 | 1.02 ± 0.05 | 4.24 ± 0.21 | 6.62 |
0.4 | 0.085 ± 0.03 | 0.110 ± 0.03 | −0.494 ± 0.01 | 1.05 ± 0.05 | 6.84 ± 0.34 | 4.28 |
BM | ||||||
0 | 0.123 ± 0.02 | 0.372 ± 0.03 | −0.476 ± 0.01 | 1.05 ± 0.05 | 4.37 ± 0.22 | 0 |
0.1 | 0.084 ± 0.03 | 0.256 ± 0.03 | −0.469 ± 0.01 | 6.67 ± 0.33 | 2.77 ± 0.14 | 36.56 |
0.2 | 0.072 ± 0.02 | 0.217 ± 0.03 | −0.463 ± 0.01 | 6.59 ± 0.33 | 2.74 ± 0.14 | 37.32 |
0.3 | 0.090 ± 0.02 | 0.237 ± 0.03 | −0.453 ± 0.01 | 6.89 ± 0.34 | 2.86 ± 0.14 | 34.48 |
0.4 | 0.101 ± 0.03 | 0.283 ± 0.02 | −0.443 ± 0.01 | 7.45 ± 0.37 | 5.67 ± 0.28 | 29.02 |
mAP | Average IoU | Precision | Recall | F1-Score | |
---|---|---|---|---|---|
mAP@0.50 | 97.11% | 60.23% | 0.77 | 0.98 | 0.86 |
mAP@0.75 | 50.48% | 43.34% | 0.53 | 0.66 | 0.59 |
Inhibitor Concentration (g/L) | WM | HAZ | BM |
---|---|---|---|
0 | 2.411± 0.121 | 2.950 ± 0.148 | 2.025 ± 0.101 |
0.1 | 1.032 ± 0.052 | 1.314 ± 0.065 | 1.252 ± 0.062 |
0.2 | 0.652 ± 0.003 | 0.612 ± 0.031 | 1.055 ± 0.053 |
0.3 | 0.903 ± 0.045 | 1.091 ± 0.055 | 1.837 ± 0.092 |
0.4 | 0.353 ± 0.018 | 1.245 ± 0.062 | 1.989 ± 0.099 |
Inhibitor Concentration (g/L) | WM | HAZ | BM |
---|---|---|---|
0 | 5.05 ± 0.26 | 2.44 ± 0.12 | 2.81 ± 0.14 |
0.1 | 3.09 ± 0.16 | 2.13 ± 0.11 | 2.27 ± 0.11 |
0.2 | 3.06 ± 0.15 | 1.58 ± 0.08 | 1.38 ± 0.07 |
0.3 | 3.08 ± 0.16 | 2.15 ± 0.11 | 2.57 ± 0.13 |
0.4 | 2.47 ± 0.12 | 2.30 ± 0.16 | 2.63 ± 0.13 |
Specimen | Element (wt %) | |||||
---|---|---|---|---|---|---|
C | O | Si | Cl | Mn | Fe | |
WM in HCl | 2.31 | 1.15 | 0.55 | 0.20 | 0.94 | 94.85 |
WM in HCl + SM | 2.40 | 30.07 | 1.57 | 0.16 | 1.03 | 43.1 |
HAZ in HCl | 2.28 | 1.16 | 0.56 | 0.22 | 0.75 | 95.02 |
HAZ in HCl + SM | 2.34 | 25.62 | 1.94 | 0.20 | 1.45 | 56.76 |
BM in HCl | 2.68 | 1.02 | 1.00 | 1.88 | 1.03 | 92.40 |
BM in HCl + SM | 2.30 | 21.79 | 1.99 | 0.98 | 1.29 | 64.25 |
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Alamsyah, F.A.; Cheng, C.-C.; Gapsari, F. Detection of Hydrogen Bubbles Produced by Corrosion Inhibition of Metal Weldment Using Machine Learning. Appl. Sci. 2024, 14, 266. https://doi.org/10.3390/app14010266
Alamsyah FA, Cheng C-C, Gapsari F. Detection of Hydrogen Bubbles Produced by Corrosion Inhibition of Metal Weldment Using Machine Learning. Applied Sciences. 2024; 14(1):266. https://doi.org/10.3390/app14010266
Chicago/Turabian StyleAlamsyah, Fikrul Akbar, Chi-Cheng Cheng, and Femiana Gapsari. 2024. "Detection of Hydrogen Bubbles Produced by Corrosion Inhibition of Metal Weldment Using Machine Learning" Applied Sciences 14, no. 1: 266. https://doi.org/10.3390/app14010266
APA StyleAlamsyah, F. A., Cheng, C.-C., & Gapsari, F. (2024). Detection of Hydrogen Bubbles Produced by Corrosion Inhibition of Metal Weldment Using Machine Learning. Applied Sciences, 14(1), 266. https://doi.org/10.3390/app14010266