Application of artificial neural networks to classification of quality of ingots after continuous casting

Władysław Zalecki

AGH University of Science and Technology.

DOI:

https://doi.org/10.7494/cmms.2001.1.0003

Abstract:

The paper present primary results on an application of artificial neural networks to predictions of the influence of technological parameters of continuous casting on the quality of casted ingots. Development of the classification method for ingots was the main objective of the work. The input data composed casting parameters and quality evaluation for 852 ingots. An analysis shows that development of models and classification methods, which will predict the quality of ingots, is possible. Beta versions of statistical and neuron models were developed. Iy is shown in the paper that an accuracy of the models depends on the quality assessment method. Neural network classification yielded better accuracy, about 96%. An attempt of development of one general classification method led to a decrease of predictions of the quality. Classification methods require further validation. The general conclusion from the work is that at this stage separate classification methods have to be created for each steel type and for each casting machine.

Cite as:

Zalecki, W. (2001). Application of artificial neural networks to classification of quality of ingots after continuous casting. Computer Methods in Materials Science, 1(1), 35 – 43. https://doi.org/10.7494/cmms.2001.1.0003

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