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 microstructures, 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 parameters 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.

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

“…In continuation of the previous work on two-phase steels, Müller et al [47] considered bainitic subclasses. The new classes required new annotations, which was the greatest difficulty in this ML implementation.…”

“…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].…”

“…The final dataset consisted of almost 4000 annotated objects, although with imbalanced classes (Table 1). [47], modified according to [47]. Reprinted from [47].…”

“…Figure 3. Seven microstructure classes considered for classification in[47], modified according to[47]. Reprinted from[47].…”

Artificial Intelligence Approaches for Energetic Materials by Design: State of the Art, Challenges, and Future Directions

“…Data‐driven methods can be classified broadly based on how they use the data: supervised learning , which requires a pairing of the input data set with a labeled output, and unsupervised learning , which only requires the input data without a labeled output; semi‐supervised learning sits in between [25]. The supervised method can be further broken down into classification (e. g., assigning the material to a general type or class of “similar” materials given an input sample [93]) or regression tasks (e. g., prediction of a property, such as the permeability of a porous material [57]). A variety of AI/ML methods that fall under these broad categories have been used for various materials applications.…”