# Deep Neural Networks for Defects Detection in Gas Metal Arc Welding

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## Abstract

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## 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 ($l{r}_{ini}$) | $0.1$ |

Decay | 6000 |

${\beta}_{1}$ | $0.9$ |

${\beta}_{2}$ | $0.99$ |

$\u03f5$ | 1 × 10${}^{-7}$ |

Epochs | 300 |

Step per epoch | 35 |

Training size (% on the total) | $85\%$ |

Validation size (% on the total) | $10\%$ |

Core NVIDIA CUDA | 896 |

Boost Clock (MHz) | 1665 |

Base Clock (MHz) | 1485 |

Memory speed (Gbps) | 8 |

Compute capability | $7.5$ |

Microarchitecture | Turing |

Final validation loss | $0.16$ |

Final training loss | $0.18$ |

Test accuracy | $93.6\%$ |

Inference time (ms) | $123.42$ |

Final validation loss | $0.13$ |

Final training loss | $0.15$ |

Test accuracy | $94.7\%$ |

Inference time (ms) | $93.9$ |

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## Share and Cite

**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Nele, 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