HEAT-TREATMENT CLASSIFICATION OF STEELS WITH NONDESTRUCTIVE EDDY CURRENT TESTING USING NEURAL NETWORKS

Eddy current-nondestructive techniques are increasingly present in industry because of the growing quality control demand. In addition to the classical crack detection, eddy currents permit physic and metallurgic properties detection in steels. The impedance values of one eddy-current-generating coil permit to differentiate steel pieces with different heat treatments. These impedance values can be processed with Artificial Neural Networks (ANNs) to implement automatic and efficient classifiers. In this article two ANN classifiers that processed monofrequency impedances and one ANN classifiers that processed multifrequency impedances are compared. The impedances were extracted from two steel samples sets with different heat treatments. The predominant microstructure in the first set was martensite and bainite and in the second one was perlite. As experimental results, the monofrequency classification accuracy rate was 90% while the multifrequency classification reached 99.9%, the theoretical computing workload of the best multifrequency classifier was between 33% and 50% lower than the best monofrequency classifier, and the multifrequency classification execution time was 22% smaller than the measured time using the monofrequency methods.