{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00227884","sets":["1164:6390:11093:11330"]},"path":["11330"],"owner":"44499","recid":"227884","title":["ブレード加工における高精度表面粗さ予測に向けた形状特徴量抽出手法の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-09-18"},"_buckets":{"deposit":"8737830e-c1e5-4405-89f3-2109f50004d1"},"_deposit":{"id":"227884","pid":{"type":"depid","value":"227884","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"ブレード加工における高精度表面粗さ予測に向けた形状特徴量抽出手法の検討","author_link":["607760","607759","607756","607758","607752","607754","607755","607757","607749","607750","607753","607751"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ブレード加工における高精度表面粗さ予測に向けた形状特徴量抽出手法の検討"},{"subitem_title":"Extraction of Shape Features for Investigation of Optimal Cutting Conditions in Blade Machining","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"生産現場とセンシング技術","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2023-09-18","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"長崎大学"},{"subitem_text_value":"長崎大学"},{"subitem_text_value":"長崎大学"},{"subitem_text_value":"長崎大学"},{"subitem_text_value":"長崎大学"},{"subitem_text_value":"長崎大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Nagasaki University","subitem_text_language":"en"},{"subitem_text_value":"Nagasaki University","subitem_text_language":"en"},{"subitem_text_value":"Nagasaki University","subitem_text_language":"en"},{"subitem_text_value":"Nagasaki University","subitem_text_language":"en"},{"subitem_text_value":"Nagasaki University","subitem_text_language":"en"},{"subitem_text_value":"Nagasaki University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/227884/files/IPSJ-CDS23038006.pdf","label":"IPSJ-CDS23038006.pdf"},"date":[{"dateType":"Available","dateValue":"2025-09-18"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CDS23038006.pdf","filesize":[{"value":"971.7 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"47"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"4d0cd0d8-93dc-4541-9aec-4fde50df7183","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"西薗, 洸成"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"江下, 翔"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"國武, 龍清"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"宮島, 洋文"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"荒井, 研一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小林, 透"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kosei, Nishizono","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kakeru, Eshita","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryusei, Kunitake","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hirofumi, Miyazima","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kenichi, Arai","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Toru, Kobayashi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12628327","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8604","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"航空機は効率的な飛行を目指して現在も改良が続けられており，特に注目されているのがエンジンを構成するブレードである．ブレードは表面のわずかな凹凸さえもエンジンの動作に影響を与えるため，ブレードの形状に合った高能率かつ高品位な加工条件を見つけることが求められている．本研究では，ブレードの最適な加工条件を見つける前段階として，ブレードごとの形状の違いを表現することを目的とした．具体的には 5 つの加工条件に加え，「形状」という条件項目を追加し，表面粗さを予測することで最適な加工条件の調査に役立てる．今回は画像分類の過程で抽出した特徴量を「形状」条件とすることを考えた．画像分類には多視点画像に対応した深層学習手法である MVCNN を用いた．MVCNN は，特徴を強調できる CNN が用いられており，多視点画像を用いることで，異なる視点からの変化に対して頑健な特徴抽出を行うことができる．しかし，今回のブレードは形状が非常に類似しており，画像の中には違いが全く現れないものもあった．そのため，ブレード同士の形状の違いを正確に判別し，画像分類を行うことは困難であった．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Aircraft continue to be improved for efficient flight, with particular attention being paid to the blades that make up the engine. Since even the slightest irregularity in the blade surface affects the operation of the engine, it is necessary to find highly efficient and high-quality cutting conditions that suit the blade geometry. The objective of this study was to express the differences in blade geometry as a preliminary step to finding optimal cutting conditions for blades. Specifically, in addition to the five cutting conditions, a condition item called \"shape\" was added to predict surface roughness to help investigate optimal cutting conditions. This time, we considered using the features extracted in the image classification process as the \"shape\" condition. MVCNN, a deep learning method for multi-view images, was used for image classification. However, the blades in this study had very similar shapes, and some of the images showed no differences at all. Therefore, it was difficult to accurately discriminate the shape differences between blades and classify the images.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"5","bibliographic_titles":[{"bibliographic_title":"研究報告コンシューマ・デバイス＆システム（CDS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-09-18","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"6","bibliographicVolumeNumber":"2023-CDS-38"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":227884,"updated":"2025-01-19T12:01:39.507380+00:00","links":{},"created":"2025-01-19T01:27:08.097246+00:00"}