ログイン
言語:

WEKO3

  • トップ
  • ランキング
To
lat lon distance
To

Field does not validate



インデックスリンク

インデックスツリー

メールアドレスを入力してください。

WEKO

One fine body…

WEKO

One fine body…

アイテム

{"_buckets": {"deposit": "7fedf870-112f-4ca1-b4ce-e268cf151485"}, "_deposit": {"id": "27953", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "27953"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00027953", "sets": ["322"]}, "author_link": ["91016", "91017"], "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2019-01-28", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "670", "bibliographicPageStart": "661", "bibliographicVolumeNumber": "744", "bibliographic_titles": [{"bibliographic_title": "Materials Science and Engineering: A", "bibliographic_titleLang": "en"}]}]}, "item_10_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "The design of new materials with useful properties is becoming increasingly important. Machine-learning tools Materials Genome Integration System Phase and Property Analysis (MIPHA) and rMIPHA (based on the R programming environment) have been independently developed to accelerate the process of materials discovery via a data-driven materials research approach. In the present work, MIPHA and rMIPHA are applied to steel, where machine-learning-based 2D/3D microstructural analysis, direct analysis of property predictions, and properties-to-microstructure inverse analysis were conducted. The results demonstrate that the prediction models deliver satisfactory performance. The inverse exploration of microstructures related to desired target properties (e.g., stress–strain curve, tensile strength, and total elongation) was realized. MIPHA and rMIPHA are still under improvement. The microstructure-to-processing inverse analysis is expected to be realized in the future.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_10_description_5": {"attribute_name": "内容記述", "attribute_value_mlt": [{"subitem_description": "ファイル公開:2021-01-28", "subitem_description_language": "ja", "subitem_description_type": "Other"}]}, "item_10_publisher_32": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "Elsevier", "subitem_publisher_language": "en"}]}, "item_10_relation_11": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type": "isVersionOf", "subitem_relation_type_id": {"subitem_relation_type_id_text": "https://doi.org/10.1016/j.msea.2018.12.049", "subitem_relation_type_select": "DOI"}}]}, "item_10_rights_12": {"attribute_name": "権利", "attribute_value_mlt": [{"subitem_rights": "© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/", "subitem_rights_language": "en"}]}, "item_10_select_15": {"attribute_name": "著者版フラグ", "attribute_value_mlt": [{"subitem_select_item": "author"}]}, "item_10_source_id_61": {"attribute_name": "ISSN(print)", "attribute_value_mlt": [{"subitem_source_identifier": "0921-5093", "subitem_source_identifier_type": "PISSN"}]}, "item_1615787544753": {"attribute_name": "出版タイプ", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_ab4af688f83e57aa", "subitem_version_type": "AM"}]}, "item_access_right": {"attribute_name": "アクセス権", "attribute_value_mlt": [{"subitem_access_right": "open access", "subitem_access_right_uri": "http://purl.org/coar/access_right/c_abf2"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Wang, Zhi-Lei", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "91016", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Adachi, Yoshitaka", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "91017", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2021-01-28"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "paper.pdf", "filesize": [{"value": "1.7 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_note", "mimetype": "application/pdf", "size": 1700000.0, "url": {"label": "paper", "objectType": "fulltext", "url": "https://nagoya.repo.nii.ac.jp/record/27953/files/paper.pdf"}, "version_id": "418f344a-9cfd-40ee-9247-dd60e65dff70"}]}, "item_keyword": {"attribute_name": "キーワード", "attribute_value_mlt": [{"subitem_subject": "3D microstructural analysis", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Property prediction", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Inverse analysis", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Machine learning", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Steels", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Property prediction and properties-to-microstructure inverse analysis of steels by a machine-learning approach", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Property prediction and properties-to-microstructure inverse analysis of steels by a machine-learning approach", "subitem_title_language": "en"}]}, "item_type_id": "10", "owner": "1", "path": ["322"], "permalink_uri": "http://hdl.handle.net/2237/00030151", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2019-05-09"}, "publish_date": "2019-05-09", "publish_status": "0", "recid": "27953", "relation": {}, "relation_version_is_last": true, "title": ["Property prediction and properties-to-microstructure inverse analysis of steels by a machine-learning approach"], "weko_shared_id": -1}
  1. B200 工学部/工学研究科
  2. B200a 雑誌掲載論文
  3. 学術雑誌

Property prediction and properties-to-microstructure inverse analysis of steels by a machine-learning approach

http://hdl.handle.net/2237/00030151
http://hdl.handle.net/2237/00030151
a0411280-7289-4e6d-8350-b34c6a9d5e5e
名前 / ファイル ライセンス アクション
paper.pdf paper (1.7 MB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2019-05-09
タイトル
タイトル Property prediction and properties-to-microstructure inverse analysis of steels by a machine-learning approach
言語 en
著者 Wang, Zhi-Lei

× Wang, Zhi-Lei

WEKO 91016

en Wang, Zhi-Lei

Search repository
Adachi, Yoshitaka

× Adachi, Yoshitaka

WEKO 91017

en Adachi, Yoshitaka

Search repository
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
権利
言語 en
権利情報 © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
キーワード
主題Scheme Other
主題 3D microstructural analysis
キーワード
主題Scheme Other
主題 Property prediction
キーワード
主題Scheme Other
主題 Inverse analysis
キーワード
主題Scheme Other
主題 Machine learning
キーワード
主題Scheme Other
主題 Steels
抄録
内容記述 The design of new materials with useful properties is becoming increasingly important. Machine-learning tools Materials Genome Integration System Phase and Property Analysis (MIPHA) and rMIPHA (based on the R programming environment) have been independently developed to accelerate the process of materials discovery via a data-driven materials research approach. In the present work, MIPHA and rMIPHA are applied to steel, where machine-learning-based 2D/3D microstructural analysis, direct analysis of property predictions, and properties-to-microstructure inverse analysis were conducted. The results demonstrate that the prediction models deliver satisfactory performance. The inverse exploration of microstructures related to desired target properties (e.g., stress–strain curve, tensile strength, and total elongation) was realized. MIPHA and rMIPHA are still under improvement. The microstructure-to-processing inverse analysis is expected to be realized in the future.
言語 en
内容記述タイプ Abstract
内容記述
内容記述 ファイル公開:2021-01-28
言語 ja
内容記述タイプ Other
出版者
言語 en
出版者 Elsevier
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
出版タイプ
出版タイプ AM
出版タイプResource http://purl.org/coar/version/c_ab4af688f83e57aa
DOI
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.1016/j.msea.2018.12.049
ISSN(print)
収録物識別子タイプ PISSN
収録物識別子 0921-5093
書誌情報 en : Materials Science and Engineering: A

巻 744, p. 661-670, 発行日 2019-01-28
著者版フラグ
値 author
戻る
0
views
See details
Views

Versions

Ver.1 2021-03-01 10:28:36.815055
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Cite as

エクスポート

OAI-PMH
  • OAI-PMH JPCOAR
  • OAI-PMH DublinCore
  • OAI-PMH DDI
Other Formats
  • JSON
  • BIBTEX

Confirm


Powered by WEKO3


Powered by WEKO3