DC Field | Value | Language |
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dc.contributor.author | Song, KC | - |
dc.contributor.author | Jang, PY | - |
dc.contributor.author | Cho, HB | - |
dc.contributor.author | Jun, CH | - |
dc.date.accessioned | 2016-03-31T13:05:41Z | - |
dc.date.available | 2016-03-31T13:05:41Z | - |
dc.date.created | 2009-02-28 | - |
dc.date.issued | 2002-10 | - |
dc.identifier.issn | 0740-817X | - |
dc.identifier.other | 2002-OAK-0000002674 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/19057 | - |
dc.description.abstract | The Model Predictive Control (MPC) method has been widely adopted as a useful tool to keep quality on target in manufacturing processes. However, the conventional MPC methods are inadequate for large-scale manufacturing processes particularly in the presence of disturbances. The goal of this paper is to propose a Partial Least Square (PLS)-based MPC methodology to accommodate the characteristics of a large-scale manufacturing process. The detailed objectives are: (i) to identify a reliable prediction model that handles the large-scale "short and fat" data; (ii) to design an effective control model that both maximizes the required quality and minimizes the labor costs associated with changing the process parameters; and (iii) to develop an efficient optimization algorithm that reduces the computational burden of the large-scale optimization. The case study and experimental results demonstrate that the presented MPC methodology provides the set of optimal process parameters for quality improvement. In particular, the quality deviations are reduced by 99.4%, the labor costs by 84.2%, and the computational time by 98.8%. As a result, the proposed MPC method will save on both costs and time in achieving the desired quality for a large-scale manufacturing process. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.relation.isPartOf | IIE TRANSACTIONS | - |
dc.title | Partial least square-based model predictive control for large-scale manufacturing processes | - |
dc.type | Article | - |
dc.contributor.college | 산업경영공학과 | - |
dc.identifier.doi | 10.1023/A:1015784809887 | - |
dc.author.google | Song, KC | - |
dc.author.google | Jang, PY | - |
dc.author.google | Cho, HB | - |
dc.author.google | Jun, CH | - |
dc.relation.volume | 34 | - |
dc.relation.issue | 10 | - |
dc.relation.startpage | 881 | - |
dc.relation.lastpage | 890 | - |
dc.contributor.id | 10083567 | - |
dc.relation.journal | IIE TRANSACTIONS | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | IIE TRANSACTIONS, v.34, no.10, pp.881 - 890 | - |
dc.identifier.wosid | 000175947600003 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 890 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 881 | - |
dc.citation.title | IIE TRANSACTIONS | - |
dc.citation.volume | 34 | - |
dc.contributor.affiliatedAuthor | Jun, CH | - |
dc.identifier.scopusid | 2-s2.0-0036778680 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 13 | - |
dc.type.docType | Article | - |
dc.relation.journalWebOfScienceCategory | Engineering, Industrial | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
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