Automatic Si phase extraction from microscopic images of Al-Si alloys by unsupervised machine learning and supervised deep learning

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成果类型：

期刊论文

作者：

Bo, Guowei;Zhou, Hui;Wang, Chenyang*;Zhang, Chipeng;Deng, Cuiling;...

通讯作者：

Wang, Chenyang;Peng, ZR;Mao, GL

作者机构：

[Zhang, Chipeng; Li, Wei; Zhou, Hui; Jiang, Dapeng; Bo, Guowei; Deng, Cuiling] Changsha Univ Sci & Technol, Coll Energy & Power Engn, Changsha 410114, Peoples R China.

[Bo, Guowei; Sun, Youping] Guangxi Univ Sci & Technol, Guangxi Key Lab Automobile Components & Vehicle Te, Liuzhou 545006, Peoples R China.

[Peng, ZR; Wang, Chenyang; Peng, Zirong; Bo, Guowei; Wang, CY] Tech Univ Munich, Chair Mat Engn Addit Mfg, Dept Mat Engn, Boltzmannstr 15, D-85748 Garching, Germany.

[Mao, Guoling] China North Engine Res Inst, Natl Key Lab Vehicle Power Syst, Tianjin 300400, Peoples R China.

[Jiang, Fulin] Hunan Univ, Coll Mat Sci & Engn, Changsha 410082, Peoples R China.

通讯机构：

[Mao, GL ] C

[Peng, ZR ; Wang, CY] T

Tech Univ Munich, Chair Mat Engn Addit Mfg, Dept Mat Engn, Boltzmannstr 15, D-85748 Garching, Germany.

China North Engine Res Inst, Natl Key Lab Vehicle Power Syst, Tianjin 300400, Peoples R China.

语种：

英文

关键词：

Al-Si alloy;Microstructural classification;Automatic phase extraction;Unsupervised machine learing;Supervised deep learing

期刊：

Materials Today Communications

ISSN：

2352-4928

年：

2025

卷：

页码：

111468

DOI：

10.1016/j.mtcomm.2024.111468

基金类别：

CRediT authorship contribution statement Guoling Mao: acquisition, Resources, Software, Writing – review & editing. Cuiling Deng: Writing – original draft, Software, Methodology, Data curation. Guowei Bo: Writing – original draft, Validation, Software, Methodology, acquisition, Data curation, Conceptualization. Chipeng Zhang: Software, Methodology, Data curation, Writing – review & editing. Chengyang Wang: Writing – original draft, Validation, Data curation, Conceptualization. Dapeng Jiang: Resources, Investigation,

机构署名：

本校为第一机构

院系归属：

能源与动力工程学院

摘要：

Microstructural classification based on microscopic images are mostly done manually by human experts, which is time-consuming and generally leads to uncertainties due to subjectivity. In this work, machine learning and deep learning are used to automatically retrieve the useful morphology information of Si phase in Al-Si alloys which are widely used as various automotive components. Concretely, both clean mircographs without oxidization and noisy micrographs with oxidization are prepared under optical microscopy. Then an unsupervised machine le...

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Automatic Si phase extraction from microscopic images of Al-Si alloys by unsupervised machine learning and supervised deep learning

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