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Your search keyword '"Arroyo, Valentín"' showing total 7 results
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7 results on '"Arroyo, Valentín"'

6. 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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7. 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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