Reproducible Quantification of the Microstructure of Complex Quenched and Quenched and Tempered Steels Using Modern Methods of Machine Learning

Abstract

Current conventional methods of evaluating microstructures are characterized by a high degree of subjectivity and a lack of reproducibility. Modern machine learning (ML) approaches have already shown great potential in overcoming these challenges. Once trained with representative data in combination with objective ground truth, the ML model is able to perform a task properly in a reproducible and automated manner. However, in highly complex use cases, it is often not possible to create a definite ground truth. This study addresses this problem using the underlying showcase of microstructures of highly complex quenched and quenched and tempered (Q/QT) steels. A patch-wise classification approach combined with a sliding window technique provides a solution for segmenting entire microphotographs where pixel-wise segmentation is not applicable since it is hardly feasible to create reproducible training masks. Using correlative microscopy, consisting of light optical microscope (LOM) and scanning electron microscope (SEM) micrographs, as well as corresponding data from electron backscatter diffraction (EBSD), a training dataset of reference states that covers a wide range of microstructures was acquired in order to train accurate and robust ML models in order to classify LOM or SEM images. Despite the enormous complexity associated with the steels treated here, classification accuracies of 88.8% in the case of LOM images and 93.7% for high-resolution SEM images were achieved. These high accuracies are close to super-human performance, especially in consideration of the reproducibility of the automated ML approaches compared to conventional methods based on subjective evaluations through experts.

Overview: Machine Learning for Segmentation and Classification of Complex Steel Microstructures

“…It is to be assumed that ML models can handle variances well for tasks with clearly separable foreground and background (e.g., segmentation of grain boundaries), whereas variances become more critical the more complex the task is (e.g., differentiation of complex and similar microstructures such as lower bainite and tempered martensite [30]). Nevertheless, it is important to understand metallographic processes in terms of the variances that may occur and to restrict variances if necessary.…”

“…A prominent example of correlative microscopy is the combination of light optical microscopy (LOM) and/or scanning electron microscopy (SEM) images capturing the visual appearance of the microstructure with electron backscatter diffraction (EBSD) maps, the latter providing complementary structural information like misorientation parameters, grain and phase boundaries, etc. [25,30,33,34]. As the initial measured crystallographic phases and orientations from EBSD are not based on visual appearance to a human expert eye, they can be regarded as objective measurement data and thereby as an ideal complementary source of information.…”

“…In the meantime, ML methods have been applied to a wide range of microstructure analysis tasks, and it is difficult to summarize them concisely. Without claiming to be comprehensive, it can be stated that mainly metallic materials are dealt with, and there is still a clear focus on steel microstructures [1,25,30,46,47]. However, there are also case studies on non-ferrous metals such as copper [48], titanium [49][50][51] or magnetic compounds [52].…”

“…For quenched and tempered steels, the complexity of the analysis is further increased, partly because there are no clearly separable foreground and background in the micrographs. In the low-to medium-carbon steels investigated by Bachmann et al [30]., the microstructure constituents martensite, tempered martensite, lower bainite and upper bainite, which can all be present simultaneously in one micrograph, should be distinguished.…”

“…In addition, the EBSD parameters could be used to verify existing steel classification schemes or even define alternative schemes using unsupervised learning. [30]. Reprinted from [30].…”