Abstract
Jominy hardness profiles of steels were predicted from chemical composition and austenitizing temperature using an artificial neural network. The neural network was trained using some 4000 examples, covering a wide range of steel compositions. The performance of the neural network is examined as a function of the network architecture, the number of alloying elements, and the number of data sets used for training. A well-trained network predicts the Jominy hardness profile with an average error of about 2 HRC. Special attention was devoted to the effect of boron on hardenability. A network trained using data only from boron steels produced results similar to those of a network trained using all data available. The accuracy of the predictions of the model is compared with that of an analytical model for hardenability and with that of a partial least- squares model using the same set of data.
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Vermeulen, W.G., van der Wolk, P.J., de Weijer, A.P. et al. Prediction of jominy hardness profiles of steels using artificial neural networks. JMEP 5, 57–63 (1996). https://doi.org/10.1007/BF02647270
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DOI: https://doi.org/10.1007/BF02647270