Gil del Val, Alain
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Gil del Val
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Alain
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Ingeniería Mecánica, Energética y de Materiales
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Publication Open Access Monitoring of blind rivets installations: contributions from the manufacturing chain and time-series imaging(Elsevier, 2025-10-01) Penalva Oscoz, Mariluz; Gil del Val, Alain; Martín, Ander; Villanueva Roldán, Pedro; Uralde Jiménez, Virginia; Veiga Suárez, Fernando; Ingeniería; Ingeniaritza; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaFastening is a crucial operation in the aircraft manufacturing cycle, and the demand for automated solutions has grown in recent years. Blind rivets are particularly suitable for automation due to their ease of use. However, fastening with blind rivets requires indirect evaluation of the formed head for in-line quality monitoring. This study presents two approaches to address this problem. Firstly, an analysis of the drilling-riveting chain assesses the impact of the previous operation on riveting outcomes. Secondly, time-dependent signals from the riveting process are coded into images and analysed using deep learning techniques. Despite some limitations, both methods for monitoring blind riveting have demonstrated high precision and accuracy values above 0.9, with 1 indicating perfect precision or accuracy, suggesting that they can reliably predict the quality of rivet installations.Publication Open Access Application-oriented data analytics in large-scale metal sheet bending(MDPI, 2023) Penalva Oscoz, Mariluz; Martín, Ander; Martínez, Víctor; Veiga Suárez, Fernando; Gil del Val, Alain; Ballesteros Egüés, Tomás; Favieres Ruiz, Cristina; Ingeniería; IngeniaritzaThe sheet-metal-forming process is crucial in manufacturing various products, including pipes, cans, and containers. Despite its significance, controlling this complex process is challenging and may lead to defects and inefficiencies. This study introduces a novel approach to monitor the sheet-metal-forming process, specifically focusing on the rolling of cans in the oil-and-gas sector. The methodology employed in this work involves the application of temporal-signal-processing and artificial-intelligence (AI) techniques for monitoring and optimizing the manufacturing process. Temporal-signal-processing techniques, such as Markov transition fields (MTFs), are utilized to transform time series data into images, enabling the identification of patterns and anomalies. synamic time warping (DTW) aligns time series data, accommodating variations in speed or timing across different rolling processes. K-medoids clustering identifies representative points, characterizing distinct phases of the rolling process. The results not only demonstrate the effectiveness of this framework in monitoring the rolling process but also lay the foundation for the practical application of these methodologies. This allows operators to work with a simpler characterization source, facilitating a more straightforward interpretation of the manufacturing process.