Intelligent system for material quality control using impact-echo testing

This paper introduces an intelligent system to discern the quality of materials inspected by the impact-echo technique. The system includes a hardware setup to inspect parallelepiped-shape materials and a procedure to classify the material depending on its quality condition. Four levels of classification with different grades of knowledge about the material defects are approached: material condition, kind of defect, defect orientation, and defect dimension. The number of classes (material qualities) in the lowest classification level is 12. The procedure is applied on signals coming from 3D finite element simulations and lab experiments with aluminium specimens. The classification procedure is performed using frequency features and the classification algorithms: LDA, MLP, and an algorithm based on mixtures of independent component analyzers. We show the best performance to model the impact-echo data is obtained by the ICA mixture model.