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