RT info:eu-repo/semantics/article
T1 Enhancing quality control in die-casting with ensemble-based computer vision methods
A1 Mielgo Martín, Paula
A1 Bregón Bregón, Aníbal
A1 Alonso González, Carlos Javier
A1 Martínez Prieto, Miguel Angel
A1 López Gómez, Daniel
A1 Pulido Junquera, José Belarmino
K1 Deep learning
K1 Die casting
K1 Ensemble
K1 Computer vision
K1 33 Ciencias Tecnológicas
AB The transition towards Industry 4.0 has led to a significant increase in the adoption of smart manufacturing, where advanced technologies, such as Artificial Intelligence and Machine Learning, are used to optimize production processes. Quality control in manufacturing presents significant challenges, particularly in detecting non-visible defects. This paper proposes a novel approach to improve quality assurance in die-casting machines for car engine block production through thermographic image analysis. Specifically, we verify whether thermal patterns in the mold, captured immediately after the part is extracted, can serve as an indicator of internal defects in manufactured components, thereby avoiding the need for expensive and time-consuming leak tests. Our approach employs a stacking ensemble as its core. The ensemble integrates Convolutional Neural Networks and Vision Transformers, leveraging their complementary strengths for defect detection. An ensemble and threshold selection process is then carried out to identify optimal classifiers for defective and non-defective parts. Experimental results based on thermographic images from a mold used in the manufacture of 4-cylinder engine blocks demonstrate that the proposed framework can ensure the internal quality of up to 63.3% of components with high confidence. This result enables a significant reduction in reliance on leak tests, illustrating the viability of a real-time, cost-effective decision-making process that reduces bottlenecks and enhances overall manufacturing efficiency.
PB Elsevier
SN 0952-1976
YR 2026
FD 2026
LK https://uvadoc.uva.es/handle/10324/84228
UL https://uvadoc.uva.es/handle/10324/84228
LA eng
NO Engineering Applications of Artificial Intelligence, 2026, vol. 177, p. 114850
NO Producción Científica
DS UVaDOC
RD 21-abr-2026