Developing a model for hardness prediction in water-quenched and tempered AISI 1045 steel through an artificial neural network

Autor:	Shalaleh Jalali, Samad Taghizadeh, Aydin Salimiasl, Asghar Safarian
Rok vydání:	2013
Předmět:	Materials science Artificial neural network business.industry Drop (liquid) Computer Science::Neural and Evolutionary Computation Metallurgy Experimental data Structural engineering Tempering business Maximum error Model validation
Zdroj:	Materials & Design. 51:530-535
ISSN:	0261-3069
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2199b9a580af7a9241240786c1240a0c https://doi.org/10.1016/j.matdes.2013.04.038 Zobrazit plný text záznamu Full Text from ScienceDirect