Presented works investigates a possibility of using modeling based on artificial neural network for prediction of liquidus temperatures of low-alloyed steels. Paper describes the methodology of creating such model by tools incorporated in commercial software MATLAB. Neural network is trained, validated and tested and previously unseen data measured by DTA method are used as new input data. Results are then compared to those measured and calculated by commonly used software for such applications like IDS and Thermo-Calc. Performance of these three modeling approaches is discussed.Keywords: artificial neural networks, liquidus temperature of steel, MATLAB, Thermo-Calc, IDS
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