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3. Optimization of Step Times for ASTM F1624 Methodology Applied to Small Punch Tests in Aggressive Environments.

Author

Andrea, Laura, Arroyo, Borja, Álvarez, Jose Alberto, Gutiérrez-Solana, Federico, Cicero, Sergio, and Guilbert, Elise

Subjects
SULFURIC acid, STEEL, PLATINUM, ANODES
Abstract

Threshold stress in aggressive environments is usually determined by tests under constant load, which are very time-consuming, so the incremental loading technique published in the ASTM F1624 standard was created to solve this issue. This approach has been recently applied to Small Punch tests, but it requires an optimization of the incremental step times, which is carried out in this work. Three medium- and high-strength quenched and tempered steels of 35, 50 and 60 HRC are exposed to three different cathodic polarization environments of 1, 5 and 10 mA/cm2 in 1N H2SO4 acid electrolyte with a Platinum anode, studying in each case three different step durations of one-quarter, one-sixth and one-eighth of the ones indicated in ASTM F1624. Optimal step times for Small Punch tests are derived from this work as one-sixth of the ones recommended in ASTM F1624 for tensile specimens, which are 20 min and 40 min for steps 1–10 and 11–20, respectively, in the case of 33 ≤ HRC < 45 steels, 10 min and 20 min for steps 1–10, 11–20 in the case of 45 ≤ HRC < 55 steels, and 10 min for steps 1–20 in the case of HRC ≥ 55 steels. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Prediction of Non-Metallic Inclusions in Steel Wires for Tire Reinforcement by means of Machine Learning Algorithms.

Author

Cuartas, Miguel, Ruiz, Estela, Ferreño, Diego, Setién, Jesús, Arroyo, Valentín, and Gutiérrez-Solana, Federico

Subjects
MACHINE learning, STEEL wire, ARTIFICIAL neural networks, STEEL founding, QUALITY control
Abstract

This study was aimed at developing a reliable Machine Learning algorithm to classify castings of steel for tire reinforcement depending on the number and properties of inclusions, experimentally determined. 855 castings were available for training, validation and testing. 140 parameters are monitored during fabrication, which are the features of the analysis; the output is 1 or 0 depending on whether the casting is rejected or not. The following algorithms have been employed: Logistic Regression, K-Nearest Neighbors, Support Vector Classifier, Random Forests, AdaBoost, Gradient Boosting and Artificial Neural Networks. The reduced value of the rejection rate implies that classification must be carried out on an imbalanced dataset. Resampling methods and specific scores for imbalanced datasets (Recall, Precision and AUC rather than Accuracy) were used. Random Forest was the most successful method providing an area under the curve in the test set of 0.85. No significant improvements were detected after resampling. It has been proved that this tool allows the samples with a higher probability of being rejected to be selected, improving the effectiveness of the quality control. In addition, the optimized Random Forest has enabled to identify the most important features, which have been satisfactorily interpreted on a metallurgical basis. [ABSTRACT FROM AUTHOR]

Published
2019
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15. Investigation through Artificial Neural Networks on the Influence of Shot Peening on the Hardness of ASTM TX304HB Stainless Steel.

Author

Ferreño, Diego, González, Ruth, Carrascal, Isidro A., Cuartas, Miguel, García, Diego, Eraña, Rubén, Gutiérrez-Solana, Federico, and Arroyo, Valentín

Abstract

The present research is aimed at obtaining and experimentally validating an artificial neural network to predict the hardness of TX304HB steel tubes subjected to shot peening. The experimental scope consisted of 228 tubes. Seven variables were considered as input parameters: rotation speed, line speed, material flow, air pressure, the size of the nozzle, and the internal diameter of the tubes; the experimental data demonstrated the need for considering the material bulk hardness as an input variable. One specimen from each tube was taken and subjected to Vickers microhardness tests at a depth of 40 µm from the interior circumference as well as a depth beyond the influence of the shot peening (bulk condition). The hardness was proven to follow Gaussian distribution. Therefore, a neural network was designed and tuned to provide the mean and standard deviation of the hardness for each of the combinations of input variables. The neural networks designed in this way were able to faithfully reproduce the experimental results. Several statistical parameters were determined to measure the goodness of the fitting. Thus, the correlation between experimental and predicted numerical values of mean hardness yields R² = 0.7651 and a mean absolute percentage error of 1.547% for the training data set and 0.7402 and 2.054% for the test data set. The corresponding values for the prediction of the standard deviation are R² = 0.4713 and 17.946% for the training set and R² = 0.6847 and 17.071% for the test set. [ABSTRACT FROM AUTHOR]

Published
2021
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16. El gobierno de las Universidades desde la perspectiva de los retos inmediatos de la educación superior

Author

Gutiérrez-Solana, Federico

Subjects
Declaración de Lisboa, competitividad, Europa, financiación, gestión, calidad, excelencia
Abstract

Las Universidades europeas, para ser más funcionales respecto al importante papel que la sociedad les exige, tendrán que ver mejorada de forma notable su financiación. Paralelamente, deberán diseñar y dotarse de una mejora organizativa que garantice una gestión más eficaz de sus recursos; para, con todo ello, mejorar su atractivo en la formación de las personas y en la aplicación del conocimiento al mundo empresarial e industrial, es decir, incrementar su capacidad de generar conocimiento y transmitirlo y ponerlo en valor en el tejido social.Estas tres necesidades de futuro (financiación como base, gestión en la ejecución y atracción como objetivo) deben ser afrontadas por el gobierno de las universidades mediante sendas ideas directrices: fomentar la calidad, transformar la gobernanza e incrementar la financiación, tanto pública como privada.AbstractThe higher education finance must be increased and improved in order to enhance the European universities performance about the social role they are impelled. At the same time, they have to design and create an organizational reform in terms of management efficacy, as a part of their attractiveness in educative services and knowledge appliableness in industrial and corporative sectors. They must increase their capability to create knowledge and to transmit it and transformer it into a social value.Finance, management and attractiveness as future needs, they must be presented at the universities government by means of some clear lines: quality assurance, governance improvement and increasing public and private finance.

Published
2016

17. Políticas universitarias para una nueva década: problemas y oportunidades

Author

Gutiérrez-Solana, Federico

Subjects
gestión del conocimiento, sistema de financiación y rendición de cuentas, Sociedad del Conocimiento, apertura e internacionalización, sistema de gobernanza, Responsabilidad social de la Universidad
Abstract

Para las universidades europeas, 2020 es el nuevo horizonte. El Comunicado de Lovaina de abril de 2009 y la Declaración de Budapest-Viena sobre el Espacio Europeo de Educación Superior en marzo de 2010, junto con el informe Tendencias 2010 de la Asociación de la Universidad Europea, señalan las principales líneas de trabajo para la próxima década. Los conceptos clave son mantener una perspectiva amplia y social-humanista, un sólido compromiso con la calidad y la excelencia, una voluntad de establecer fuertes lazos con otras universidades, no sólo europeas, y una clara idea de la conexión interna entre la investigación científica y el rendimiento académico de las universidades. En este contexto, la Universidad española deberá afrontar importantes retos en áreas como la gobernanza, la financiación y la innovación y transferencia del conocimiento. Bolonia ha terminado, ha comenzado Lovaina.AbstractFor European universities, 2020 is the new horizon. The Communiqué of Leuven/Lovaine-la-Neuve in April 2009 and the Budapest-Vienna Declaration on the European Higher Education Area in Mars 2010 mark, together with the dossier Trends 2010 of the European University Association, the main lines of work for the next decade. The key concepts are to keep a broad and social-humanistic vision; a solid compromise with quality and excellence; a will of establishing strong links with others universities non only Europeans; and a clear idea about the inner connection between scientific research and academic performance of universities. In this context, the Spanish University will have to confront important challenges in areas as the governance, the financing and the innovation and transfer of the knowledge. Bolonia is over, Leuven/Lovaine has begun.

Published
2016

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