Deep Neural Networks for Defects Detection in Gas Metal Arc Welding
Abstract
:1. Introduction
Review of GMAW Process and Defects
- Metallurgical discontinuities, which are problematic primarily due to the drop in mechanical properties of the joint and are typically identified through nondestructive testing.
- Metallurgical inhomogeneity, which is more complex to identify and assess.
2. Materials and Methods
2.1. A Brief Summary of Artificial Neural Networks
2.2. Development Workflow
2.2.1. Case Study and Data Collection
2.2.2. Choosing an Architecture
2.2.3. Training
3. Results
3.1. Architecture A
3.2. Architecture B
4. Conclusions and Future Developments
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
WPS | Welding Procedure Specification |
GMAW | Gas Metal Arc Welding |
ANN | Artificial Neural Networks |
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Total samples | 2655 |
Batch size | 64 |
Initial learning rate () | |
Decay | 6000 |
1 × 10 | |
Epochs | 300 |
Step per epoch | 35 |
Training size (% on the total) | |
Validation size (% on the total) |
Core NVIDIA CUDA | 896 |
Boost Clock (MHz) | 1665 |
Base Clock (MHz) | 1485 |
Memory speed (Gbps) | 8 |
Compute capability | |
Microarchitecture | Turing |
Final validation loss | |
Final training loss | |
Test accuracy | |
Inference time (ms) |
Final validation loss | |
Final training loss | |
Test accuracy | |
Inference time (ms) |
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Nele, L.; Mattera, G.; Vozza, M. Deep Neural Networks for Defects Detection in Gas Metal Arc Welding. Appl. Sci. 2022, 12, 3615. https://doi.org/10.3390/app12073615
Nele L, Mattera G, Vozza M. Deep Neural Networks for Defects Detection in Gas Metal Arc Welding. Applied Sciences. 2022; 12(7):3615. https://doi.org/10.3390/app12073615
Chicago/Turabian StyleNele, Luigi, Giulio Mattera, and Mario Vozza. 2022. "Deep Neural Networks for Defects Detection in Gas Metal Arc Welding" Applied Sciences 12, no. 7: 3615. https://doi.org/10.3390/app12073615