Abstract Scope |
Artificial intelligence (AI) and machine learning (ML) have now made their way into materials science, too and are omnipresent. Especially for microstructure segmentation and classification, for which simple, traditional methods like thresholding or manual judgment by human experts are still common, AI and ML offer new potentials and promise decisive improvements. The segmentation, classification and following quantification of the microstructure are the foundation for establishing process-microstructure-property correlations, which in turn are the basis for developing or optimizing materials. This work deals with the automated, objective, and reproducible classification of the carbon-rich second phase objects in multi-phase steels. In total, seven microstructure classes, including bainite subclasses, are considered which can all be present simultaneously in one micrograph. Based on SEM images, textural features and morphological parameters are calculated and classified with a support vector machine with an accuracy of 89.2 % regarding the area of second phase objects. |