Interpretability of Deep Learning Classification for Low-Carbon Steel Microstructures

Abstract

In this paper, a model is developed to identify the microstructure of low-carbon steel by deep learning. In classifying steel microstructures using a machine learning model, predictions are interpreted using local interpretable model-agnostic explanations (LIME) for the first time. The constructed model can accurately distinguish between eight microstructure types, including upper bainite, lower bainite, martensite, and their mixed structures. The model accuracy is 94.1% when individually predicted and 97.9% when predicted by majority vote. In addition, as a result of interpreting the predictions of the model by LIME, it is evident that the recognition criterion of the constructed model is partially consistent with the classic recognition criterion.

Fig. 8 Input SEM images and LIME output images of T1 (Upper Bainite), T6 (Lower Bainite), and T8 (Martensite). (a), (b), and (c) ((d), (e), and (f)) correspond to the input (output) images of T1, T6, and T8, respectively.