RT journal article
T1 Automated Fillet Weld Inspection Based on Deep Learning from 2D Images
A1 Díaz Cano, Ignacio
A1 Morgado Estévez, Arturo
A1 Rodríguez Corral, José María
A1 Medina Coello, Pablo
A1 Salvador Domínguez, Blas
A1 Álvarez Alcón, Miguel
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
A2 Ingeniería Informática
A2 Ingeniería Mecánica y Diseño Industrial
K1 CNN
K1 surface inspection welding
K1 shipbuilding
K1 FCAW
K1 GMAW
K1 welding defects
K1 deep learning
K1 geometric deep learning
K1 YOLO
AB 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.
PB MDPI
SN 2076-3417
YR 2025
FD 2025-01-17
LK http://hdl.handle.net/10498/35336
UL http://hdl.handle.net/10498/35336
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026