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Multiresponse optimization of multistage manufacturing process using a patient rule induction method

Authors
Lee, Dong HeeYang, Jin-Kyung
Issue Date
Jul-2018
Publisher
Springer Verlag
Keywords
Data mining; Multiresponse optimization; Multistage manufacturing process; Patient rule induction method
Citation
Lecture Notes in Computer Science, v.10960 LNCS, pp 598 - 610
Pages
13
Indexed
SCOPUS
Journal Title
Lecture Notes in Computer Science
Volume
10960 LNCS
Start Page
598
End Page
610
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/203569
DOI
10.1007/978-3-319-95162-1_41
ISSN
0302-9743
1611-3349
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.
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