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 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.Publication Embargo Finite element method for minimizing geometric error in the bending of large sheets(Springer, 2024-10-31) Gil del Val, Alain; Penalva Oscoz, Mariluz; Veiga Suárez, Fernando; El Moussaoui, Bilal; Ingeniería; IngeniaritzaMinimizing geometric error in the bending of large sheets remains a challenging endeavor in the industrial environment. This specific industrial operation is characterized by protracted cycles and limited batch sizes. Coupled with extended cycle times, the process involves a diverse range of dimensions and materials. Given these operational complexities, conducting practical experimentation for data extraction and control of industrial process parameters proves to be unfeasible. To gain insights into the process, finite element models serve as invaluable tools for simulating industrial processes for reducing experimental cost. Consequently, the primary objective of this research endeavor is to develop an intelligent finite element model capable of providing operators with pertinent information regarding the optimal range of key parameters to mitigate geometric error in the bending of large sheets. This prediction model is based on response surface method to predict the bending diameter of the pipe taking into account three main process parameters: the plate thickness, the length, and the roll displacement. These results present promising prospects for the automation of the industrial process because the average geometric error in curvature is recorded at 0.97%, thereby meeting the stringent industrial requirement for achieving such bending with minimal equivalent plastic deformation.