Data mining algorithm for manufacturing process control
dc.contributor.author | Zakarian, Armen | en_US |
dc.contributor.author | Sadoyan, Hovhannes | en_US |
dc.contributor.author | Mohanty, Pravansu | en_US |
dc.date.accessioned | 2006-09-11T16:35:24Z | |
dc.date.available | 2006-09-11T16:35:24Z | |
dc.date.issued | 2006-03 | en_US |
dc.identifier.citation | Sadoyan, Hovhannes; Zakarian, Armen; Mohanty, Pravansu; (2006). "Data mining algorithm for manufacturing process control." The International Journal of Advanced Manufacturing Technology 28 (3-4): 342-350. <http://hdl.handle.net/2027.42/45889> | en_US |
dc.identifier.issn | 0268-3768 | en_US |
dc.identifier.issn | 1433-3015 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/45889 | |
dc.description.abstract | In this paper, a new data mining algorithm based on the rough sets theory is presented for manufacturing process control. The algorithm extracts useful knowledge from large data sets obtained from manufacturing processes and represents this knowledge using “if/then” decision rules. Application of the data mining algorithm developed in this paper is illustrated with an industrial example of rapid tool making (RTM). RTM is a technology that adopts rapid prototyping (RP) techniques, such as spray forming, and applies them to tool and die making. A detailed discussion on how to control the output of the manufacturing process using the results obtained from the data mining algorithm is also presented. Compared to other data mining methods, such decision trees and neural networks, the advantage of the proposed approach is its accuracy, computational efficiency, and ease of use. | en_US |
dc.format.extent | 392390 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Springer-Verlag | en_US |
dc.subject.other | Decision Rules | en_US |
dc.subject.other | Rough Sets | en_US |
dc.subject.other | Industrial and Production Engineering | en_US |
dc.subject.other | Mechanical Engineering | en_US |
dc.subject.other | Engineering | en_US |
dc.subject.other | Computer-Aided Engineering (CAD, CAE) and Design | en_US |
dc.subject.other | Production/Logistics | en_US |
dc.subject.other | Data Mining | en_US |
dc.subject.other | Manufacturing Process Control | en_US |
dc.title | Data mining algorithm for manufacturing process control | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Management | en_US |
dc.subject.hlbsecondlevel | Information and Library Science | en_US |
dc.subject.hlbsecondlevel | Industrial and Operations Engineering | en_US |
dc.subject.hlbsecondlevel | Computer Science | en_US |
dc.subject.hlbsecondlevel | Economics | en_US |
dc.subject.hlbtoplevel | Business | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Mechanical Engineering, University of Michigan – Dearborn, Dearborn, MI, 48128, U.S.A. | en_US |
dc.contributor.affiliationum | Department of Industrial and Manufacturing Systems Engineering, University of Michigan – Dearborn, USA | en_US |
dc.contributor.affiliationum | Department of Industrial and Manufacturing Systems Engineering, University of Michigan – Dearborn, USA | en_US |
dc.contributor.affiliationumcampus | Dearborn | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/45889/1/170_2004_Article_2367.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/s00170-004-2367-1 | en_US |
dc.identifier.source | The International Journal of Advanced Manufacturing Technology | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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