Efficient Phase Segmentation of Light-Optical Microscopy Images of Highly Complex Microstructures Using a Correlative Approach in Combination with Deep Learning Techniques

Abstract

Reliable microstructure characterization is essential for establishing process–microstructure–property links and effective quality control. Traditional manual microstructure analysis often struggles with objectivity, reproducibility, and scalability, particularly in complex materials. Machine learning methods offer a promising alternative but are hindered by the challenge of assigning an accurate and consistent ground truth, especially for complex microstructures. This paper introduces a methodology that uses correlative microscopy—combining light optical microscopy, scanning electron microscopy, and electron backscatter diffraction (EBSD)—to create objective, reproducible pixel-by-pixel annotations for ML training. In a semi-automated manner, EBSD-based annotations are employed to generate an objective ground truth mask for training a semantic segmentation model for quantifying simple light optical micrographs. The training masks are directly derived from raw EBSD data using modern deep learning methods. By using EBSD-based annotations, which incorporate crystallographic and misorientation data, the correctness and objectivity of the training mask creation can be assured. The final approach is capable of reproducibly and objectively differentiating bainite and martensite in optical micrographs of complex quenched steels. Through the reduction in the microstructural evaluation to light optical micrographs as the simplest and most widely used method, this way of quantifying microstructures is characterized by high efficiency as well as good scalability.

Keywords

• quantification
• microstructure
• steels
• correlative microscopy
• machine learning
• semantic segmentation