Prediction The Jominy Curves by Means of Neural Networks

T. Filetin, D. Majetić, I. Žmak:
Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Croatia

Accurate prediction of hardenability based on the chemical composition is very important for steel production as well as for its users. An attempt has been made to establish a non-linear static discrete-time neuron model, the so-called Static Elementary Processor (SEP). Based on the SEP neurons, a Static Multi Layer Perceptron Neural Network is proposed to predict a Jominy hardness curve from chemical composition. To accelerate the convergence of proposed static error-back propagation learning algorithm, the momentum method is applied. The learning results are presented in terms that are insensitive to the learning data range and allow easy comparison with other learning algorithms, independent of machine architecture or simulator implementation.
In the learning process datasets with 121 heats are used – comprising samples from 40 steel grades with different chemical composition. The mean error between measured and predicted hardness data and standard deviation for testing dataset (60 heats – samples from 203 heats in question) is comparable with other published methods of prediction. The additional testing of three smaller groups – Cr-steels; Cr-Ni-Mo (Ni-Cr-Mo) steels for hardening and tempering and Cr-Mo, Cr-Ni (Ni-Cr), Cr-Ni-Mo (Ni-Cr-Mo) steels for carburizing shows better accuracy then by testing with heterogeneous dataset.

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