Microstructural Classification of Bainitic Subclasses in Low-Carbon Multi-Phase Steels Using Machine Learning Techniques

Abstract

With its excellent property combinations and ability to specifically adjust tailor-made microstructures, steel is still the world’s most important engineering and construction material. To fulfill ever-increasing demands and tighter tolerances in today’s steel industry, steel research remains indispensable. The continuous material development leads to more and more complex microstruc tures, which is especially true for steel designs that include bainitic structures. This poses new challenges for the classification and quantification of these microstructures. Machine learning (ML) based microstructure classification offers exciting potentials in this context. This paper is concerned with the automated, objective, and reproducible classification of the carbon-rich second phase objects in multi-phase steels by using machine learning techniques. For successful applications of ML-based classifications, a holistic approach combining computer science expertise and material science domain knowledge is necessary. Seven microstructure classes are considered: pearlite, martensite, and the bainitic subclasses degenerate pearlite, debris of cementite, incomplete transformation product, and upper and lower bainite, which can all be present simultaneously in one micrograph. Based on SEM images, textural features (Haralick parameters and local binary pattern) and morphological parame ters are calculated and classified with a support vector machine. Of all second phase objects, 82.9% are classified correctly. Regarding the total area of these objects, 89.2% are classified correctly. The reported classification can be the basis for an improved, sophisticated microstructure quantification, enabling process–microstructure–property correlations to be established and thereby forming the backbone of further, microstructure-centered material development.