engleski

Predicting the hardenability of steels using neural network

An attempt has been made to establish a non-linear statis discrete-time neuron model, teh so-called Static Elementary Preocessor (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 tge 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 dadtaset (60 heats - samples from 203 heats in question) is comparable with other published methods of prediction.

hardenability of steels ; neural networks ; prediction

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