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Multiresponse optimization of multistage manufacturing process using a patient rule induction method
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lee, Dong Hee | - |
| dc.contributor.author | Yang, Jin-Kyung | - |
| dc.date.accessioned | 2024-12-20T07:39:59Z | - |
| dc.date.available | 2024-12-20T07:39:59Z | - |
| dc.date.issued | 2018-07 | - |
| dc.identifier.issn | 0302-9743 | - |
| dc.identifier.issn | 1611-3349 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/203569 | - |
| dc.description.abstract | Most of manufacturing industries produce products through a series of sequential processes. This is called multistage process. It is often difficult to optimize the multistage process due to the correlation between stages. Therefore, the relationships among the multiple processes should be considered in the multistage process optimization. Also, the processes often have multiple responses, thus, it is important to optimize multiple responses of multistage process. In these days, data mining techniques have been widely applied to process optimization. The proposed method attempts to optimize multiresponse of multistage process using a particular data mining method, called patient rule induction method. The proposed method obtains an optimal setting of input variables directly from the operational data in which multiple responses are optimized, simultaneously. The proposed approach is explained and illustrated by a step-by-step procedure with a case example. | - |
| dc.format.extent | 13 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | Springer Verlag | - |
| dc.title | Multiresponse optimization of multistage manufacturing process using a patient rule induction method | - |
| dc.type | Article | - |
| dc.publisher.location | 미국 | - |
| dc.identifier.doi | 10.1007/978-3-319-95162-1_41 | - |
| dc.identifier.scopusid | 2-s2.0-85049903841 | - |
| dc.identifier.bibliographicCitation | Lecture Notes in Computer Science, v.10960 LNCS, pp 598 - 610 | - |
| dc.citation.title | Lecture Notes in Computer Science | - |
| dc.citation.volume | 10960 LNCS | - |
| dc.citation.startPage | 598 | - |
| dc.citation.endPage | 610 | - |
| dc.type.docType | Conference Paper | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.subject.keywordPlus | Optimization | - |
| dc.subject.keywordPlus | Process control | - |
| dc.subject.keywordPlus | Data mining methods | - |
| dc.subject.keywordPlus | Manufacturing industries | - |
| dc.subject.keywordPlus | Multiresponse optimization | - |
| dc.subject.keywordPlus | Multistage manufacturing process | - |
| dc.subject.keywordPlus | Multistage process | - |
| dc.subject.keywordPlus | Rule Induction Methods | - |
| dc.subject.keywordPlus | Sequential process | - |
| dc.subject.keywordPlus | Step by step procedure | - |
| dc.subject.keywordPlus | Data mining | - |
| dc.subject.keywordAuthor | Data mining | - |
| dc.subject.keywordAuthor | Multiresponse optimization | - |
| dc.subject.keywordAuthor | Multistage manufacturing process | - |
| dc.subject.keywordAuthor | Patient rule induction method | - |
| dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-319-95162-1_41 | - |
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