"Detection of Welding Flaws with MLP Neural Network and Case Based Reasoning"
Inter'l Journal of Intelligent Automation and Soft Computing,
Vol. 9, No. 4, pp. 259-267, 2003.
by T. Warren Liao, Evangelos Triantaphyllou, and P.C. Chang
The correct detection of welding flaws is important to the successful development of an
automated weld inspection system. As a continuation of our previous efforts, this study
investigates the performance of multi-layer perception (MLP) neural networks and case
based reasoning (CBR) individually as well as their combined use. It is found t hat better
performance is attained by all methods tested in this study than that was obtained by the
fuzzy clustering methods employed before. For each method, the effect of using different
parameters is also investigated and discussed. An improvement of CBR performance is
not guaranteed when the MLP NN based attribute weighing is used. In addition, none of
the three combination-of-multiple-classifiers methods (majority voting, Borda count, and
arithmetic averaging) tested improve the performance of the best individual classifiers.
Welding flaws, MLP neural networks, Case based reasoning, Attribute weighting,
Combination of multiple classifiers.