A generic high-throughput microstructure classification and quantification method for regular SEM images of complex steel microstructures combining EBSD labeling and deep learning

Shen, Chunguang; Wang, Chenchong; Huang, Minghao; Xu, Ning; Zwaag, Sybrand van der; Xu, Wei

doi:10.1016/j.jmst.2021.04.009

Cited by 40 publications

(12 citation statements)

References 40 publications

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“…Such an understanding is important for transferring the classification workflows to industrial applications. On the other hand, EBSD could also be used to automatically generate annotations for the microstructure classes, as suggested in [25] and done in [20]. This could allow, with a set of correlative micrographs and EBSD-based annotations, the training of a bainite classification scheme, which uses only SEM or even only LM images during application.…”

Section: Discussionmentioning

confidence: 99%

“…Approaches for a more objective ground truth assignment for ML segmentation or classification include Shen et al [20], who use electron backscatter diffraction (EBSD) to generate annotations for DL segmentation of steel microstructures. Müller et al [8] propose the use of EBSD, reference samples, and unsupervised learning as supporting methods for assigning the ground truth, demonstrated on a bainite case study.…”

Section: Introductionmentioning

confidence: 99%

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

Müller

Britz

Staudt

et al. 2021

Metals

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Müller

Britz

Staudt

et al. 2021

Metals

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“…However, it is difficult to obtain the corresponding OM images from the same area of the same steel sample in the study of Azimi, and the model can only distinguish the two‐phase microstructure where the first phase (background) is ferrite, which makes the model represent insufficient generalization ability. Recently, Shen et al [ 27 ] combined EBSD labeling and deep learning to segment different phases, and quantify phase content and grain size on a dual‐phase (DP) steel and a quenching and partitioning (Q&P) steel. In addition, they proposed an extensible microstructural quantification method combining the physical metallurgy (PM)‐guided data augmentation and DL to construct a comprehensive dataset.…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification of the Multiphase Microstructures of Steels based on ASPP‐FCN

Xie

Weigang

Fan

et al. 2023

steel research int.

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The properties of steels are often closely related to the category and distribution of their microstructures. However, the classification and quantitative analysis of multiphase steel are mostly performed manually, which is time‐consuming and laborious. Moreover, due to the variant experience of experts, the classification results of one image will be different. In this article, an automatic classification model is proposed to identify the multiphase microstructures of steels by means of semantic segmentation, which realizes the automatic statistics of the relative content of each crystalline phase. Semantic segmentation can assign each pixel in an image to one of a set of predefined categories, which can accomplish two tasks concurrently: phase identification and segmentation. We propose an improved fully convolutional network ASPP‐FCN, and carry out comparative experiments with different networks, including FCN, DeepLab v3+, Unet, Enet and PSPnet. The final pixel accuracy of ASPP‐FCN in each category is more than 95%; and the mean intersection over union is over 80%, the results demonstrate that ASPP‐FCN has better segmentation effect in the automatic recognition of the multiphase microstructures of steels.

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“…A model that can rapidly determine the target performance of complex micro/nanostructures and easily generalize them to similar systems is required to identify laser-textured surfaces with superhydrophobic properties among large design spaces within reasonable time frames. Deep learning, which has been widely used in materials science, − can independently extract and learn features and make intelligent decisions to rapidly establish the regression relationship between process parameters or microstructure (inputs) and target performance (outputs). Using a convolutional neural network (CNN), Kim et al effectively developed a prediction model of the stress–strain curves of unidirectional composites with complex microstructures, presenting an interesting example .…”

Section: Introductionmentioning

confidence: 99%

Multimodal Deep-Learning Framework for Accurate Prediction of Wettability Evolution of Laser-Textured Surfaces

Zhang

Yang

Zhao

et al. 2023

ACS Appl. Mater. Interfaces

Self Cite

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The lengthy process through which laser-textured surfaces transform from hydrophilic to hydrophobic severely restricts their practical applications. Accurately predicting the wettability evolution curve is crucial; however, developing a reliable prediction model remains challenging. Herein, a data-driven multimodal deep-learning framework was developed, in which multimodal data of micro/nanostructure morphology images, composition distribution images, and time information are effectively coupled and fed into a convolutional neural network (CNN). Rich data input and in-depth data mining make the framework more robust, achieving accurate prediction of the wettability evolution curves of various typical micro/nanostructures. Additionally, accurate prediction of input images with varying magnifications and untrained laser-textured surfaces demonstrates the generalizability of the multimodal CNN framework. The visualization results of the convolution layer confirmed the rationality of the information learned by the model. Additionally, the proposed multimodal CNN framework was successfully utilized to investigate the optimization process. Further, a laser-textured surface with a shorter evolution period and a larger final contact angle was realized. The proposed multimodal CNN framework offers an efficient and cost-effective method for predicting the wettability evolution curves and exploring the optimization processes, enhancing the application potential of laser micro/nanofabrication of superhydrophobic surfaces.

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A generic high-throughput microstructure classification and quantification method for regular SEM images of complex steel microstructures combining EBSD labeling and deep learning

Cited by 40 publications

References 40 publications

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

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

Automatic Identification of the Multiphase Microstructures of Steels based on ASPP‐FCN

Multimodal Deep-Learning Framework for Accurate Prediction of Wettability Evolution of Laser-Textured Surfaces

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