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Article: Machining process sequencing with fuzzy expert system and genetic algorithms

TitleMachining process sequencing with fuzzy expert system and genetic algorithms
Authors
KeywordsCost-tolerance
Fuzzy expert system
Genetic algorithms
Process planning
Process sequencing
Uncertainty
Issue Date2003
PublisherSpringer-Verlag London Ltd. The Journal's web site is located at http://link.springer.de/link/service/journals/00366/
Citation
Engineering With Computers, 2003, v. 19 n. 2-3, p. 191-202 How to Cite?
AbstractTraditional process planning systems are usually established in a deterministic framework that can only deal with precise information. However, in a practical manufacturing environment, decision making frequently involves uncertain and imprecise information. This paper describes a fuzzy approach for solving the process selection and sequencing problem under uncertainty. The proposed approach comprises a two-stage process for machining process selection and sequencing. The two stages are called intra-feature planning and inter-feature planning, respectively. According to the feature precedence relationship of a machined part, the intra-feature planning module generates a local optimal operation sequence for each feature element. This is based on a fuzzy expert system incorporated with genetic algorithms for machining cost optimization according to the cost-tolerance relationship. Manufacturing resources such as machines, tools, and fixtures are allocated to each selected operation to form an Operation-Machine-Tool (OMT) unit in the manufacturing resources allocation module. Finally, inter-feature planning generates a global OMT sequence. A genetic algorithm with fuzzy numbers and fuzzy arithmetic is developed to solve this global sequencing problem.
Persistent Identifierhttp://hdl.handle.net/10722/74335
ISSN
2015 Impact Factor: 1.46
2015 SCImago Journal Rankings: 0.540
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWong, TNen_HK
dc.contributor.authorChan, LCFen_HK
dc.contributor.authorLau, HCWen_HK
dc.date.accessioned2010-09-06T07:00:19Z-
dc.date.available2010-09-06T07:00:19Z-
dc.date.issued2003en_HK
dc.identifier.citationEngineering With Computers, 2003, v. 19 n. 2-3, p. 191-202en_HK
dc.identifier.issn0177-0667en_HK
dc.identifier.urihttp://hdl.handle.net/10722/74335-
dc.description.abstractTraditional process planning systems are usually established in a deterministic framework that can only deal with precise information. However, in a practical manufacturing environment, decision making frequently involves uncertain and imprecise information. This paper describes a fuzzy approach for solving the process selection and sequencing problem under uncertainty. The proposed approach comprises a two-stage process for machining process selection and sequencing. The two stages are called intra-feature planning and inter-feature planning, respectively. According to the feature precedence relationship of a machined part, the intra-feature planning module generates a local optimal operation sequence for each feature element. This is based on a fuzzy expert system incorporated with genetic algorithms for machining cost optimization according to the cost-tolerance relationship. Manufacturing resources such as machines, tools, and fixtures are allocated to each selected operation to form an Operation-Machine-Tool (OMT) unit in the manufacturing resources allocation module. Finally, inter-feature planning generates a global OMT sequence. A genetic algorithm with fuzzy numbers and fuzzy arithmetic is developed to solve this global sequencing problem.en_HK
dc.languageengen_HK
dc.publisherSpringer-Verlag London Ltd. The Journal's web site is located at http://link.springer.de/link/service/journals/00366/en_HK
dc.relation.ispartofEngineering with Computersen_HK
dc.subjectCost-toleranceen_HK
dc.subjectFuzzy expert systemen_HK
dc.subjectGenetic algorithmsen_HK
dc.subjectProcess planningen_HK
dc.subjectProcess sequencingen_HK
dc.subjectUncertaintyen_HK
dc.titleMachining process sequencing with fuzzy expert system and genetic algorithmsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0177-0667&volume=19&spage=191&epage=202&date=2003&atitle=Machining+process+sequencing+with+fuzzy+expert+system+and+genetic+algorithmsen_HK
dc.identifier.emailWong, TN: tnwong@hku.hken_HK
dc.identifier.authorityWong, TN=rp00192en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s00366-003-0260-4en_HK
dc.identifier.scopuseid_2-s2.0-0141741168en_HK
dc.identifier.hkuros85966en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0141741168&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume19en_HK
dc.identifier.issue2-3en_HK
dc.identifier.spage191en_HK
dc.identifier.epage202en_HK
dc.identifier.isiWOS:000185800800010-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridWong, TN=55301015400en_HK
dc.identifier.scopusauthoridChan, LCF=36985743300en_HK
dc.identifier.scopusauthoridLau, HCW=7201497785en_HK

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