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Application of Bayesian networks to diagnostics of hot dip galvanized coasts

Adrian A., Durak J., Mrzygłód B., Kluska-Nawarecka S.

Archives of Foundry Engineering

2007

Vol. 7, iss. 3

247-252

This study presents an output of the application of a probabilistic method of inference based on Bayes' rule in the diagnosis of defects formed during hot-dip galvanising process of casting products. Bayesian cause-effect network for given group of surface defects and its causes was build. Many factors causing defects was taken into consideration like: technological parameters, technological nodes and character of cause. The advantages and drawbacks of a probabilistic method of representation of the incomplete and uncertain empirical knowledge were highlighted.