The detection and classification method of sub-macroscopic defects inside steel with an ultrasonic testing and CatBoost-based stacking model

Abstract

Abstract With the increasing demand for advanced steel, the internal cleanness of steel becomes an important evaluation indicator of material quality. Sub-macroscopic defects are randomly distributed inside steel materials, which have seriously affected material stability and fatigue life because they are not covered by existing testing standards. Besides, the existing detection methods generally have problems such as low efficiency and complexity. In this paper, we propose a non-destructive inclusion testing and classification framework based on ultrasonic testing experiments, signal feature extraction and machine-learning methods. Under the optimal experimental detection conditions we found through experiments, a large-scale sub-macroscopic inclusion signal data set is established to realize the classification of defects. Moreover, empirical mode decomposition (EMD) and other feature extraction algorithms are applied to further boost the model performance. We propose a CatBoost-based stacking fused model named Stacked-CBT, which obtains state-of-the-art experimental results with an accuracy rate of 86.65% and demonstrates that the proposed framework is feasible to classify the sub-macroscopic inclusion signals. To the best of our knowledge, there is no previous study in this field that has acquired such a large amount of experimental sub-macroscopic signal data while taking into consideration classification-specific designs.