File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Intelligent approaches to tolerance allocation and manufacturing operations selection in process planning

TitleIntelligent approaches to tolerance allocation and manufacturing operations selection in process planning
Authors
KeywordsComputer aided process planning
Genetic algorithm
Hopfield neural network
Manufacturing operations selection
Tolerance allocation
Issue Date2001
PublisherElsevier SA. The Journal's web site is located at http://www.elsevier.com/locate/jmatprotec
Citation
Journal Of Materials Processing Technology, 2001, v. 117 n. 1-2, p. 75-83 How to Cite?
AbstractIn the modern manufacturing environment, alternative sets of manufacturing operations can normally be generated for machining one feature of a part. Each set of manufacturing operations results in a specific manufacturing cost in terms of the allocated tolerances, and requires a specific set of manufacturing resources, such as machines, fixtures/jigs and cutting tools. In this paper, the problems of allocating tolerances to the manufacturing operations and selecting exactly one representative from the alternative sets of manufacturing operations for machining one feature of the part are formulated. The purpose is to minimize, for all the features to be machined, the sum of the costs of the selected sets of manufacturing operations and the dissimilarities in their manufacturing resource requirements. The techniques of the genetic algorithm and the Hopfield neural network are adopted as possible approaches to solve these problems. The genetic algorithm is utilized to generate the optimal tolerance for each of the manufacturing operations, and the Hopfield neural network is adopted to solve the manufacturing operations selection problem. An illustrative example is given to demonstrate the efficiency of the proposed approaches. Indeed, the proposed approaches show the potential of working towards the optimal solutions to the tolerance allocation problem and the manufacturing operations selection problem in process planning. © 2001 Elsevier Science B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/74570
ISSN
2015 Impact Factor: 2.359
2015 SCImago Journal Rankings: 1.464
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorMing, XGen_HK
dc.contributor.authorMak, KLen_HK
dc.date.accessioned2010-09-06T07:02:38Z-
dc.date.available2010-09-06T07:02:38Z-
dc.date.issued2001en_HK
dc.identifier.citationJournal Of Materials Processing Technology, 2001, v. 117 n. 1-2, p. 75-83en_HK
dc.identifier.issn0924-0136en_HK
dc.identifier.urihttp://hdl.handle.net/10722/74570-
dc.description.abstractIn the modern manufacturing environment, alternative sets of manufacturing operations can normally be generated for machining one feature of a part. Each set of manufacturing operations results in a specific manufacturing cost in terms of the allocated tolerances, and requires a specific set of manufacturing resources, such as machines, fixtures/jigs and cutting tools. In this paper, the problems of allocating tolerances to the manufacturing operations and selecting exactly one representative from the alternative sets of manufacturing operations for machining one feature of the part are formulated. The purpose is to minimize, for all the features to be machined, the sum of the costs of the selected sets of manufacturing operations and the dissimilarities in their manufacturing resource requirements. The techniques of the genetic algorithm and the Hopfield neural network are adopted as possible approaches to solve these problems. The genetic algorithm is utilized to generate the optimal tolerance for each of the manufacturing operations, and the Hopfield neural network is adopted to solve the manufacturing operations selection problem. An illustrative example is given to demonstrate the efficiency of the proposed approaches. Indeed, the proposed approaches show the potential of working towards the optimal solutions to the tolerance allocation problem and the manufacturing operations selection problem in process planning. © 2001 Elsevier Science B.V. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier SA. The Journal's web site is located at http://www.elsevier.com/locate/jmatprotecen_HK
dc.relation.ispartofJournal of Materials Processing Technologyen_HK
dc.subjectComputer aided process planningen_HK
dc.subjectGenetic algorithmen_HK
dc.subjectHopfield neural networken_HK
dc.subjectManufacturing operations selectionen_HK
dc.subjectTolerance allocationen_HK
dc.titleIntelligent approaches to tolerance allocation and manufacturing operations selection in process planningen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0924-0136&volume=117&spage=75&epage=83&date=2001&atitle=Intelligent+approaches+to+tolerance+allocation+and+manufacturing+operations+selection+in+process+planningen_HK
dc.identifier.emailMak, KL:makkl@hkucc.hku.hken_HK
dc.identifier.authorityMak, KL=rp00154en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/S0924-0136(01)01154-2en_HK
dc.identifier.scopuseid_2-s2.0-0035798189en_HK
dc.identifier.hkuros71932en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035798189&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume117en_HK
dc.identifier.issue1-2en_HK
dc.identifier.spage75en_HK
dc.identifier.epage83en_HK
dc.identifier.isiWOS:000172287000013-
dc.publisher.placeSwitzerlanden_HK
dc.identifier.scopusauthoridMing, XG=7005300183en_HK
dc.identifier.scopusauthoridMak, KL=7102680226en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats