Classification of welding flaw types with fuzzy expert systems

Abstract The fuzzy expert system approach is proposed for the classification of different types of welding flaws. The fuzzy rules are generated from available examples using two different methods. The classification accuracy of fuzzy expert systems using fuzzy rules generated by the two methods is evaluated and compared. In addition, the fuzzy expert system approach is also compared with two other approaches: the fuzzy k-nearest neighbors algorithm and multi-layer perceptron neural networks, based on the bootstrap method. The results indicate that the fuzzy expert system approach outperforms all others in terms of classification accuracy.

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