{"_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}