Automated Fillet Weld Inspection Based on Deep Learning from 2D Images
This work presents an automated welding inspection system based on a neural network trained through a series of 2D images of welding seams obtained in the same study. The object detection method follows a geometric deep learning model based on convolutional neural networks. Following an extensive re...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/899 |
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| author | Ignacio Diaz-Cano Arturo Morgado-Estevez José María Rodríguez Corral Pablo Medina-Coello Blas Salvador-Dominguez Miguel Alvarez-Alcon |
| author_facet | Ignacio Diaz-Cano Arturo Morgado-Estevez José María Rodríguez Corral Pablo Medina-Coello Blas Salvador-Dominguez Miguel Alvarez-Alcon |
| author_sort | Ignacio Diaz-Cano |
| collection | DOAJ |
| description | This work presents an automated welding inspection system based on a neural network trained through a series of 2D images of welding seams obtained in the same study. The object detection method follows a geometric deep learning model based on convolutional neural networks. Following an extensive review of available solutions, algorithms, and networks based on this convolutional strategy, it was determined that the You Only Look Once algorithm in its version 8 (YOLOv8) would be the most suitable for object detection due to its performance and features. Consequently, several models have been trained to enable the system to predict specific characteristics of weld beads. Firstly, the welding strategy used to manufacture the weld bead was predicted, distinguishing between two of them (Flux-Cored Arc Welding (FCAW)/Gas Metal Arc Welding (GMAW)), two of the predominant welding processes used in many industries, including shipbuilding, automotive, and aeronautics. In a subsequent experiment, the distinction between a well-manufactured weld bead and a defective one was predicted. In a final experiment, it was possible to predict whether a weld seam was well-manufactured or not, distinguishing between three possible welding defects. The study demonstrated high performance in three experiments, achieving top results in both binary classification (in the first two experiments) and multiclass classification (in the third experiment). The average prediction success rate exceeded 97% in all three experiments. |
| format | Article |
| id | doaj-art-6f43822b948748c2917561fb04c60739 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-6f43822b948748c2917561fb04c607392025-01-24T13:21:15ZengMDPI AGApplied Sciences2076-34172025-01-0115289910.3390/app15020899Automated Fillet Weld Inspection Based on Deep Learning from 2D ImagesIgnacio Diaz-Cano0Arturo Morgado-Estevez1José María Rodríguez Corral2Pablo Medina-Coello3Blas Salvador-Dominguez4Miguel Alvarez-Alcon5Department of Computers Engineering, 11519 Cadiz, SpainDepartment of Automatic, Electronic, Computer Architecture & Communication Networks Engineering, 11519 Cadiz, SpainDepartment of Computers Engineering, 11519 Cadiz, SpainDepartment of Mechanical Engineering and Industrial Design, 11519 Cadiz, SpainDepartment of Automatic, Electronic, Computer Architecture & Communication Networks Engineering, 11519 Cadiz, SpainDepartment of Mechanical Engineering and Industrial Design, 11519 Cadiz, SpainThis work presents an automated welding inspection system based on a neural network trained through a series of 2D images of welding seams obtained in the same study. The object detection method follows a geometric deep learning model based on convolutional neural networks. Following an extensive review of available solutions, algorithms, and networks based on this convolutional strategy, it was determined that the You Only Look Once algorithm in its version 8 (YOLOv8) would be the most suitable for object detection due to its performance and features. Consequently, several models have been trained to enable the system to predict specific characteristics of weld beads. Firstly, the welding strategy used to manufacture the weld bead was predicted, distinguishing between two of them (Flux-Cored Arc Welding (FCAW)/Gas Metal Arc Welding (GMAW)), two of the predominant welding processes used in many industries, including shipbuilding, automotive, and aeronautics. In a subsequent experiment, the distinction between a well-manufactured weld bead and a defective one was predicted. In a final experiment, it was possible to predict whether a weld seam was well-manufactured or not, distinguishing between three possible welding defects. The study demonstrated high performance in three experiments, achieving top results in both binary classification (in the first two experiments) and multiclass classification (in the third experiment). The average prediction success rate exceeded 97% in all three experiments.https://www.mdpi.com/2076-3417/15/2/899CNNsurface inspection weldingshipbuildigFCAWGMAWwelding defects |
| spellingShingle | Ignacio Diaz-Cano Arturo Morgado-Estevez José María Rodríguez Corral Pablo Medina-Coello Blas Salvador-Dominguez Miguel Alvarez-Alcon Automated Fillet Weld Inspection Based on Deep Learning from 2D Images Applied Sciences CNN surface inspection welding shipbuildig FCAW GMAW welding defects |
| title | Automated Fillet Weld Inspection Based on Deep Learning from 2D Images |
| title_full | Automated Fillet Weld Inspection Based on Deep Learning from 2D Images |
| title_fullStr | Automated Fillet Weld Inspection Based on Deep Learning from 2D Images |
| title_full_unstemmed | Automated Fillet Weld Inspection Based on Deep Learning from 2D Images |
| title_short | Automated Fillet Weld Inspection Based on Deep Learning from 2D Images |
| title_sort | automated fillet weld inspection based on deep learning from 2d images |
| topic | CNN surface inspection welding shipbuildig FCAW GMAW welding defects |
| url | https://www.mdpi.com/2076-3417/15/2/899 |
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