Multi-Classifier selection-fusion framework: application to NDT of complex metallic parts

Recent advances in computational methods, material science, and manufacturing technologies reveal promising potentials for using geometrically complex parts to optimize the performance of structural systems. However, this potential has not yet been activated partly due to the immaturity of nondestructive testing (NDT) of such complex parts. Process compensated resonance testing (PCRT) is one of the methods that are in the focus of researchers for this purpose. The key to success for the PCRT approach is to use high-frequency vibration data in conjunction with statistical pattern recognition methods for supervised classification of parts in terms of their structural quality. In this paper, a multi classifier selection-fusion framework based on the Dempster-Shafer theory is proposed. Two new weighting approaches are introduced to enhance the fusion performance, and as such the classification performance. The effectiveness of the proposed framework is validated by its application to six UCI machine learning datasets and one experimental dataset collected from polycrystalline Nickel alloy first-stage turbine blades with a variety of damage features. Comparison with four state-of-the-art fusion techniques shows the good performance of the introduced classifier selection-fusion framework.