Proposal of data mining process for tool catalog data introducing machine learning
We attempt to construct a novel technology development utilizing big data such as Deep Learning in the manufacturing industry. Especially, we look at the data mining method and the tool catalog as a useful big data base which is updated by tool makers because it is easy for CAD/CAM engineers and mac...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | Japanese |
| Published: |
The Japan Society of Mechanical Engineers
2019-08-01
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| Series: | Nihon Kikai Gakkai ronbunshu |
| Subjects: | |
| Online Access: | https://www.jstage.jst.go.jp/article/transjsme/85/877/85_19-00215/_pdf/-char/en |
| _version_ | 1797989028166893568 |
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| author | Taishi SAKUMA Akihito ASAKURA Kotaro YAMADA Toshiki HIROGAKI Eiichi AOYAMA Hiroyuki KODAMA |
| author_facet | Taishi SAKUMA Akihito ASAKURA Kotaro YAMADA Toshiki HIROGAKI Eiichi AOYAMA Hiroyuki KODAMA |
| author_sort | Taishi SAKUMA |
| collection | DOAJ |
| description | We attempt to construct a novel technology development utilizing big data such as Deep Learning in the manufacturing industry. Especially, we look at the data mining method and the tool catalog as a useful big data base which is updated by tool makers because it is easy for CAD/CAM engineers and machine tool operators to obtain it in the manufacturing fields. In the present report, we proposed the visualization and consideration of cutting condition determination process based on a decision tree method which is one type of statistical analysis method for radius-endmill data base. We also developed a cutting condition prediction system with a random forest which is a type of machine learning method applying a decision tree. Moreover, we performed a case study in endmilling under deriving cutting conditions by the proposed method, which is an unknown and expanded cutting condition based on tool catalog data base. As a result, it is demonstrated that the support based on machine learning is found to be effective to select a cutting condition including an unknown cutting condition in tool catalog data base. |
| first_indexed | 2024-04-11T08:12:33Z |
| format | Article |
| id | doaj.art-749d1f7e729d4739845fb69f90f0ca94 |
| institution | Directory Open Access Journal |
| issn | 2187-9761 |
| language | Japanese |
| last_indexed | 2024-04-11T08:12:33Z |
| publishDate | 2019-08-01 |
| publisher | The Japan Society of Mechanical Engineers |
| record_format | Article |
| series | Nihon Kikai Gakkai ronbunshu |
| spelling | doaj.art-749d1f7e729d4739845fb69f90f0ca942022-12-22T04:35:16ZjpnThe Japan Society of Mechanical EngineersNihon Kikai Gakkai ronbunshu2187-97612019-08-018587719-0021519-0021510.1299/transjsme.19-00215transjsmeProposal of data mining process for tool catalog data introducing machine learningTaishi SAKUMA0Akihito ASAKURA1Kotaro YAMADA2Toshiki HIROGAKI3Eiichi AOYAMA4Hiroyuki KODAMA5Graduate School of Science and Engineering, Doshisha UniversityGraduate School of Science and Engineering, Doshisha UniversityGraduate School of Science and Engineering, Doshisha UniversityGraduate School of Science and Engineering, Doshisha UniversityGraduate School of Science and Engineering, Doshisha UniversityFaculty of Engineering, Okayama UniversityWe attempt to construct a novel technology development utilizing big data such as Deep Learning in the manufacturing industry. Especially, we look at the data mining method and the tool catalog as a useful big data base which is updated by tool makers because it is easy for CAD/CAM engineers and machine tool operators to obtain it in the manufacturing fields. In the present report, we proposed the visualization and consideration of cutting condition determination process based on a decision tree method which is one type of statistical analysis method for radius-endmill data base. We also developed a cutting condition prediction system with a random forest which is a type of machine learning method applying a decision tree. Moreover, we performed a case study in endmilling under deriving cutting conditions by the proposed method, which is an unknown and expanded cutting condition based on tool catalog data base. As a result, it is demonstrated that the support based on machine learning is found to be effective to select a cutting condition including an unknown cutting condition in tool catalog data base.https://www.jstage.jst.go.jp/article/transjsme/85/877/85_19-00215/_pdf/-char/endata miningdecision treerandom forestmachine learningradius end mill |
| spellingShingle | Taishi SAKUMA Akihito ASAKURA Kotaro YAMADA Toshiki HIROGAKI Eiichi AOYAMA Hiroyuki KODAMA Proposal of data mining process for tool catalog data introducing machine learning Nihon Kikai Gakkai ronbunshu data mining decision tree random forest machine learning radius end mill |
| title | Proposal of data mining process for tool catalog data introducing machine learning |
| title_full | Proposal of data mining process for tool catalog data introducing machine learning |
| title_fullStr | Proposal of data mining process for tool catalog data introducing machine learning |
| title_full_unstemmed | Proposal of data mining process for tool catalog data introducing machine learning |
| title_short | Proposal of data mining process for tool catalog data introducing machine learning |
| title_sort | proposal of data mining process for tool catalog data introducing machine learning |
| topic | data mining decision tree random forest machine learning radius end mill |
| url | https://www.jstage.jst.go.jp/article/transjsme/85/877/85_19-00215/_pdf/-char/en |
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