Grain size measurement in optical microstructure using support vector regression

Gajalakshmi, K; Palanivel, S.; Nalini, N; Saravanan, S.; Raghukandan, K.

doi:10.1016/j.ijleo.2017.03.052

Cited by 26 publications

(2 citation statements)

References 19 publications

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“…By contrast, unlike the rule-based methods, learning-based methods learn by task related data to get their corresponding model. Some typical machine learning methods, such as support vector regression (SVR) [9], fuzzy logic algorithm and neural network (NN) [10], are naturally thought of in grain boundary detection task. Furthermore, in recent years, the method based on deep learning has also been used in the task of grain boundary detection [11].…”

Section: B Literature Reviewmentioning

confidence: 99%

Grain Boundary Detection Based on Multi-Level Loss From Feature and Adversarial Learning

Chen

Liu

2020

IEEE Access

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The shape, size and distribution of grains play an important role in the quality of alloy materials. However, in the actual production process, the grain boundary is not always visible in the metallographic image observed by microscope. In this paper, we propose an end-to-end deep network named GF-RCF based on adversary network and feature learning to detect grain boundaries effectively. The network is guided by a novel multi-level loss through its three parts: base network, adversary network and metric module, which are used for boundary detection, boundary inpainting and feature learning respectively. Specifically, for base network, its architecture is based on richer convolution feature to achieve basic boundary detection by pixel-level loss. For adversary network, it employs the image-level adversary loss and feature matching loss to repair the invisible boundary. For metric module, it involves a feature learning strategy by comparing the features of unlabeled data with the labeled data to improve the generalization of the network. The multi-level loss refers to the total loss of pixel-level, image-level and feature-level obtained from the three subnetwork, respectively. From the perspective of machine learning, GF-RCF involves semi-supervised learning, transfer learning and generative adversarial network, which makes our model have more powerful learning ability. In experiments, GF-RCF achieves 0.8792 F1 score, surpassing other methods and reaching the state of the art.

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Section: B Literature Reviewmentioning

confidence: 99%

Grain Boundary Detection Based on Multi-Level Loss From Feature and Adversarial Learning

Chen

Liu

2020

IEEE Access

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“…Zhang et al [6] implemented fuzzy logic to extract the grain boundaries of high-strength aluminium alloy microstructure digital images, while Dengiz et al [7] combined a fuzzy logic algorithm and Neural Network (NN) algorithms for grain boundary detection of super alloy steel optical microstructure images. Gajalakshmi et al [8] developed an image processing algorithm to determine an average grain size in metallic microstructures by counting the number of grains, with the use of Otsu and Canny edge detection techniques and a support vector regression. Vanderesse et al [9] employed image processing techniques to distinguish between inter-and intra-granular delta phase precipitates in Inconel 718.…”

Section: Introductionmentioning

confidence: 99%

Determination of the Grain Size in Single-Phase Materials by Edge Detection and Concatenation

Berus

Skakun

Rajnović

et al. 2020

Metals

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This paper presents a novel approach for edge detection and concatenation. It applies the proposed method on a set of optical microscopy images of aluminium alloy Al 99.5% (ENAW1050A) samples with different grain size values. The performance of the proposed approach is evaluated based on the intercept method and compared with the manual grain size determination method. Edge detection filters have proven inefficient in grain boundaries’ detection of the presented microscopy images. To some extent only the Canny edge-detection filter was able to compute grain boundaries of lower-resolution images adequately, while the presented method proved to be superior, especially in high-resolution images. The proposed method has proven its applicability, and it implies higher automatisation and lower processing times compared to manual optical microscopy image processing.

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Metallographic Determination of the Number and Sizes of Grains Depending on Structural and Phase Changes in the Metal of Welded Steam Pipe Joints

Harashchenko

Dmytryk

Berezutskyi

et al. 2022

Lecture Notes in Mechanical Engineering

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Grain size measurement in optical microstructure using support vector regression

Cited by 26 publications

References 19 publications

Grain Boundary Detection Based on Multi-Level Loss From Feature and Adversarial Learning

Grain Boundary Detection Based on Multi-Level Loss From Feature and Adversarial Learning

Determination of the Grain Size in Single-Phase Materials by Edge Detection and Concatenation

Metallographic Determination of the Number and Sizes of Grains Depending on Structural and Phase Changes in the Metal of Welded Steam Pipe Joints

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