Greedy breadth-first-search based chain classification for nondestructive sizing of subsurface defects

Abstract

Machine learning including neural networks are useful in eddy current nondestructive evaluation for automated sizing of defects in a component or structure. Sizing of subsurface defects in an electrically conducting material using eddy current response is challenging, as the skin-effect and radial extents of magnetic fields are expected to strongly influence. Moreover, the information about all defect characteristics such as length, width, depth, and height is available within an eddy current image. Inspired by the recent developments in machine learning for multidimensional classification and their promise, this paper proposes chain classification for sizing of defects. Chain classification enables incorporation of dependency between the class variables which can enhance the performance of the machine learning algorithms. The best sequence among the class variables has been optimized using a greedy breadth-first-search (GBFS) algorithm and systematic studies have been carried out using the GBFS. Two well established machine learning classification algorithms, namely, radial basis function neural network and support vector machine have been used in chain classification. Coupling the chain classification with the GBFS, an approach for automated sizing of defects has been proposed. From modeled as well as experimentally obtained eddy current images, it has been established that the proposed approach can successfully size subsurface as well as surface defects.