Person: 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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0000-0003-1653-7702
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810218
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Publication Open Access Validation of the use of concept maps as an evaluation tool for the teaching and learning of mechanical and industrial engineering(Springer, 2024) Veiga Suárez, Fernando; Gil del Val, Alain; Iriondo, Edurne; Eslava Adot, Urko; Ingeniería; Ingeniaritza; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaThis paper presents the experimental work developed to measure the learning process through concept map analysis. The development of a concept map is requested by the students for each chapter or theme of the subject. As a result, maps from engineering courses have been analyzed. The measurements carried out consider several parameters, such as individual and team map building, student progressive knowledge level, and map complexity. Concerning the complexity analysis, the focus is qualitative, and it is based on the data extracted from the concept maps elaborated by the students. The study, conducted during the 2018-2019 academic year, included students from various academic levels and institutions, such as the Public University of Navarra UPNA and the University of the Basque Country UPV-EHU, covering first-degree students of Bachelor's Degree in Mechanical Engineering and first-degree students of Master's Degree in Industrial Engineering at UPNA, third-degree students of Bachelor's Degree in Mechanical Engineering at UPV-EHU. The data collected from 37 individual maps in Industrial Drawing, 31 group maps in Industrial Drawing, 12 individual maps in Design of Machinery, and 12 group maps in Design of Machinery, along with a control group of 79 students who did not participate in any activity, provided valuable insights into the effectiveness of concept maps for evaluating understanding levels and learning outcomes across various engineering subjects and academic levels. The learning outcome of the students is treated to obtain the level of understanding of complex systems shown by the students through the concept maps previously drawn and the questionnaire answered by each student about the achievement of learning results through the use of concept maps. This work shows the research methodology established and the learning results achieved qualitatively: measuring the maps by means of a rubric, self-assessment based on a survey, and through the questionnaires. Also, the results obtained in the final exams have been compared. From the observed results, this methodology is presented as a suitable alternative for evaluating the correct acquisition of concepts in online teaching situations.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.