# Burn-through prediction and weld depth estimation by deep learning model monitoring the molten pool in gas metal arc welding with gap fluctuation

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/burn-through-prediction-and-weld-depth-estimation-by-deep-learning-model/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Kazufumi Nomura,Koki Fukushima,T. Matsumura,Satoru Asai |
| Citations | 102 |
| DOI | 10.1016/j.jmapro.2020.10.019 |
| Fields | Engineering |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://www.sciencedirect.com/science/article/pii/S1526612520306873/pdf |
| OpenAlex ID | https://openalex.org/W3095170206 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Kazufumi Nomura](https://scholariq.org/researchers/kazufumi-nomura/)
- [Satoru Asai](https://scholariq.org/researchers/satoru-asai/)

## Paper primary topic

- [Welding Techniques and Residual Stresses](https://scholariq.org/topics/welding-techniques-and-residual-stresses/)

## Paper topics

- [Welding Techniques and Residual Stresses](https://scholariq.org/topics/welding-techniques-and-residual-stresses/)
- [Non-Destructive Testing Techniques](https://scholariq.org/topics/non-destructive-testing-techniques/)
- [Thermography and Photoacoustic Techniques](https://scholariq.org/topics/thermography-and-photoacoustic-techniques/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.