2026-04-27T21:55:12Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/842282026-04-21T19:21:28Zcom_10324_1165com_10324_931com_10324_894col_10324_1335

00925njm 22002777a 4500 dc Mielgo Martín, Paula author Bregón Bregón, Aníbal author Alonso González, Carlos Javier author Martínez Prieto, Miguel Angel author López Gómez, Daniel author Pulido Junquera, José Belarmino author 2026 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. Engineering Applications of Artificial Intelligence, 2026, vol. 177, p. 114850 0952-1976 https://uvadoc.uva.es/handle/10324/84228 10.1016/j.engappai.2026.114850 114850 Engineering Applications of Artificial Intelligence 177 Enhancing quality control in die-casting with ensemble-based computer vision methods