Optimal process adjustment by integrating production data and design of experiments
dc.contributor.author | Li, Jing | en_US |
dc.contributor.author | Xie, Hairong | en_US |
dc.contributor.author | Jin, Jionghua (Judy) | en_US |
dc.date.accessioned | 2011-04-07T18:52:05Z | |
dc.date.accessioned | 2011-04-07T18:52:05Z | |
dc.date.available | 2012-05-14T20:40:08Z | en_US |
dc.date.issued | 2011-04 | en_US |
dc.identifier.citation | Li, Jing; Xie, Hairong; Jin, Jionghua (2011). "Optimal process adjustment by integrating production data and design of experiments." Quality and Reliability Engineering International 27(3): 327-336. <http://hdl.handle.net/2027.42/83455> | en_US |
dc.identifier.issn | 0748-8017 | en_US |
dc.identifier.issn | 1099-1638 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/83455 | |
dc.description.abstract | This paper proposes a method to improve the process model estimation based on limited experimental data by making use of abundant production data and to achieve the optimal process adjustment based on the improved process model. The proposed method is called an Estimation-adjustment (EA) method. Furthermore, this paper proves three properties associated with the EA, which guarantee the feasibility and effectiveness of using EA for integrating production and experimental data for optimal process adjustment. Also, the paper develops a sequential hypothesis testing procedure for implementing the EA. The properties and implementation of the EA are demonstrated in a cotton spinning process. Copyright © 2010 John Wiley & Sons, Ltd. | en_US |
dc.publisher | John Wiley & Sons, Ltd. | en_US |
dc.subject.other | Engineering | en_US |
dc.subject.other | Electronic, Electrical & Telecommunications Engineering | en_US |
dc.title | Optimal process adjustment by integrating production data and design of experiments | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Management | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbsecondlevel | Economics | en_US |
dc.subject.hlbtoplevel | Business | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, U.S.A. | en_US |
dc.contributor.affiliationother | Industrial Engineering, Arizona State University, Tempe, AZ, U.S.A. ; Industrial Engineering, Arizona State University, Tempe, AZ, U.S.A. | en_US |
dc.contributor.affiliationother | Industrial Engineering, Arizona State University, Tempe, AZ, U.S.A. | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/83455/1/1123_ftp.pdf | |
dc.identifier.doi | 10.1002/qre.1123 | en_US |
dc.identifier.source | Quality and Reliability Engineering International | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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