Favieres Ruiz, Cristina
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Favieres Ruiz
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Cristina
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InaMat2. Instituto de Investigación en Materiales Avanzados y Matemáticas
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Publication Open Access Three-dimensional finite element modelling of sheet metal forming for the manufacture of pipe components: symmetry considerations(MDPI, 2022) Bhujangrao, Trunal; Veiga Suárez, Fernando; Penalva Oscoz, Mariluz; Costas, Adriana; Favieres Ruiz, Cristina; Ingeniería; IngeniaritzaThe manufacture of parts by metal forming is a widespread technique in sectors such as oil and gas and automotives. It is therefore important to make a research effort to know the correct set of parameters that allow the manufacture of correct parts. This paper presents a process analysis by means of the finite element model. The use case presented in this paper is that of a 3-m diameter pipe component with a thickness of 22 mm. In this type of application, poor selection of process conditions can result in parts that are out of tolerance, both in dimensions and shape. A 3D finite element model is made, and the symmetry of the tube section generated in 2D is analysed. As a novelty, an analysis of the process correction as a function of the symmetrical deformation of the material in this case in the form of a pipe is carried out. The results show a correct fitting of the model and give guidelines for manufacturing.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.