Automatic Identification and Quantitative Characterization of Primary Dendrite Microstructure Based on Machine Learning
Dendrites are important microstructures in single-crystal superalloys. The distribution of dendrites is closely related to the heat treatment process and mechanical properties of single-crystal superalloys. The primary dendrite arm spacing (PDAS) is an important length scale to describe the distribu...
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doaj-4b7b525b17e64747b71ffd7406abda132021-09-25T23:57:32ZengMDPI AGCrystals2073-43522021-09-01111060106010.3390/cryst11091060Automatic Identification and Quantitative Characterization of Primary Dendrite Microstructure Based on Machine LearningWeihao Wan0Dongling Li1Haizhou Wang2Lei Zhao3Xuejing Shen4Dandan Sun5Jingyang Chen6Chengbo Xiao7Beijing Advanced Innovation Center for Materials Genome Engineering, Central Iron & Steel Research Institute, Beijing 100081, ChinaBeijing Advanced Innovation Center for Materials Genome Engineering, Central Iron & Steel Research Institute, Beijing 100081, ChinaBeijing Advanced Innovation Center for Materials Genome Engineering, Central Iron & Steel Research Institute, Beijing 100081, ChinaBeijing Advanced Innovation Center for Materials Genome Engineering, Central Iron & Steel Research Institute, Beijing 100081, ChinaBeijing Advanced Innovation Center for Materials Genome Engineering, Central Iron & Steel Research Institute, Beijing 100081, ChinaQingdao NCS Testing & Corrosion Protection Technology Co., Ltd., Qingdao 266071, ChinaScience and Technology on Advanced High Temperature Structural Materials Laboratory, Beijing Institute of Aeronautical Materials, Beijing 100095, ChinaScience and Technology on Advanced High Temperature Structural Materials Laboratory, Beijing Institute of Aeronautical Materials, Beijing 100095, ChinaDendrites are important microstructures in single-crystal superalloys. The distribution of dendrites is closely related to the heat treatment process and mechanical properties of single-crystal superalloys. The primary dendrite arm spacing (PDAS) is an important length scale to describe the distribution of dendrites. In this work, the second-generation single crystal superalloy HT901 with a diameter of 15 mm was imaged under a metallurgical microscope. An automatic dendrite core identification and full-field quantitative statistical analysis method is proposed to automatically detect the dendrite core and calculate the local PDAS. The Faster R-CNN algorithm combined with test time augmentation (TTA) technology is used to automatically identify the dendrite cores. The local multi-directional algorithm combined with Voronoi tessellation is used to determine the local nearest neighbor dendrite and calculate the local PDAS and coordination number. The accuracy of using Faster R-CNN combined with TTA to detect the dendrite core of HT901 reaches 98.4%, which is 15.9% higher than using Faster R-CNN alone. The algorithm calculates the local PDAS of all dendrites in H901 and captures the Gaussian distribution of the local PDAS. The average PDAS determined by the Gaussian distribution is 415 μm, which is only a small difference from the average spacing <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>λ</mi><mo>¯</mo></mover></semantics></math></inline-formula> (420 μm) calculated by the traditional method. The technology analyzes the relationship between the local PDAS and the distance from the center of the sample. The local PDAS near the center of HT901 are larger than those near the edge. The results suggests that the method enables the rapid, accurate and quantitative dendritic distribution characterization.https://www.mdpi.com/2073-4352/11/9/1060single crystal superalloyTTA Faster R-CNNPDASlocal multi-directionVoronoi tessellation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Weihao Wan Dongling Li Haizhou Wang Lei Zhao Xuejing Shen Dandan Sun Jingyang Chen Chengbo Xiao |
spellingShingle |
Weihao Wan Dongling Li Haizhou Wang Lei Zhao Xuejing Shen Dandan Sun Jingyang Chen Chengbo Xiao Automatic Identification and Quantitative Characterization of Primary Dendrite Microstructure Based on Machine Learning Crystals single crystal superalloy TTA Faster R-CNN PDAS local multi-direction Voronoi tessellation |
author_facet |
Weihao Wan Dongling Li Haizhou Wang Lei Zhao Xuejing Shen Dandan Sun Jingyang Chen Chengbo Xiao |
author_sort |
Weihao Wan |
title |
Automatic Identification and Quantitative Characterization of Primary Dendrite Microstructure Based on Machine Learning |
title_short |
Automatic Identification and Quantitative Characterization of Primary Dendrite Microstructure Based on Machine Learning |
title_full |
Automatic Identification and Quantitative Characterization of Primary Dendrite Microstructure Based on Machine Learning |
title_fullStr |
Automatic Identification and Quantitative Characterization of Primary Dendrite Microstructure Based on Machine Learning |
title_full_unstemmed |
Automatic Identification and Quantitative Characterization of Primary Dendrite Microstructure Based on Machine Learning |
title_sort |
automatic identification and quantitative characterization of primary dendrite microstructure based on machine learning |
publisher |
MDPI AG |
series |
Crystals |
issn |
2073-4352 |
publishDate |
2021-09-01 |
description |
Dendrites are important microstructures in single-crystal superalloys. The distribution of dendrites is closely related to the heat treatment process and mechanical properties of single-crystal superalloys. The primary dendrite arm spacing (PDAS) is an important length scale to describe the distribution of dendrites. In this work, the second-generation single crystal superalloy HT901 with a diameter of 15 mm was imaged under a metallurgical microscope. An automatic dendrite core identification and full-field quantitative statistical analysis method is proposed to automatically detect the dendrite core and calculate the local PDAS. The Faster R-CNN algorithm combined with test time augmentation (TTA) technology is used to automatically identify the dendrite cores. The local multi-directional algorithm combined with Voronoi tessellation is used to determine the local nearest neighbor dendrite and calculate the local PDAS and coordination number. The accuracy of using Faster R-CNN combined with TTA to detect the dendrite core of HT901 reaches 98.4%, which is 15.9% higher than using Faster R-CNN alone. The algorithm calculates the local PDAS of all dendrites in H901 and captures the Gaussian distribution of the local PDAS. The average PDAS determined by the Gaussian distribution is 415 μm, which is only a small difference from the average spacing <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mover accent="true"><mi>λ</mi><mo>¯</mo></mover></semantics></math></inline-formula> (420 μm) calculated by the traditional method. The technology analyzes the relationship between the local PDAS and the distance from the center of the sample. The local PDAS near the center of HT901 are larger than those near the edge. The results suggests that the method enables the rapid, accurate and quantitative dendritic distribution characterization. |
topic |
single crystal superalloy TTA Faster R-CNN PDAS local multi-direction Voronoi tessellation |
url |
https://www.mdpi.com/2073-4352/11/9/1060 |
work_keys_str_mv |
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