Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Developing a model for hardness prediction in water-quenched and tempered AISI 1045 steel through an artificial neural network
Highlights We develop an ANN model for hardness drop prediction. Investigating the effects of tempering conditions on hardness drop value. With increasing tempering time, the hardness drop does not always increases.
Abstract The aim of the current study was to develop an artificial neural network (ANN) model to predict the hardness drop of the water-quenched and tempered AISI 1045 steel specimens, as a function of tempering temperature and time parameters. In the first stage, the effects of selected tempering parameters on the hardness drop value were investigated. In the second stage, a group of data, which have been obtained from experiments, was used for training of the ANN model. Likewise, another group of experimental data was utilized for the ANN model validation. Ultimately, maximum error of the ANN prediction was determined. The agreement between the predicted values of the ANN model with the experimental data was found to be reasonably good.
Developing a model for hardness prediction in water-quenched and tempered AISI 1045 steel through an artificial neural network
Highlights We develop an ANN model for hardness drop prediction. Investigating the effects of tempering conditions on hardness drop value. With increasing tempering time, the hardness drop does not always increases.
Abstract The aim of the current study was to develop an artificial neural network (ANN) model to predict the hardness drop of the water-quenched and tempered AISI 1045 steel specimens, as a function of tempering temperature and time parameters. In the first stage, the effects of selected tempering parameters on the hardness drop value were investigated. In the second stage, a group of data, which have been obtained from experiments, was used for training of the ANN model. Likewise, another group of experimental data was utilized for the ANN model validation. Ultimately, maximum error of the ANN prediction was determined. The agreement between the predicted values of the ANN model with the experimental data was found to be reasonably good.
Developing a model for hardness prediction in water-quenched and tempered AISI 1045 steel through an artificial neural network
Taghizadeh, Samad (Autor:in) / Safarian, Asghar (Autor:in) / Jalali, Shalaleh (Autor:in) / Salimiasl, Aydin (Autor:in)
10.04.2013
6 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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