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Conference Paper: Study of ductile fracture and preform design of upsetting process using adaptive network fuzzy inference system

TitleStudy of ductile fracture and preform design of upsetting process using adaptive network fuzzy inference system
Authors
KeywordsAdaptive-Network-Based Inference System
Elasto-Plastic Finite Element
Preform
Issue Date2003
PublisherElsevier SA. The Journal's web site is located at http://www.elsevier.com/locate/jmatprotec
Citation
Journal Of Materials Processing Technology, 2003, v. 140 n. 1-3 SPEC., p. 576-582 How to Cite?
AbstractThis paper combines adaptive-network-based inference system (ANFIS) and elasto-plastic finite element to predict the ductile fracture initiation and the preform shape of the upsetting process. From the hybrid-learning algorithm in ANFIS, it can efficiently construct rule database and optimal distribution of membership function to solve the punch stroke which causes the ductile fracture, and the preform shape which results a desired cylindrical workpiece after forming in the upsetting process. As a verification of this system, the punch stroke for ductile fracture initiation and the free boundary radius of the billet after forming are compared between ANFIS and FEM simulated results. In the ductile fracture prediction, it is proved that ANFIS can efficiently predict the ductile fracture initiation successfully for arbitrary friction coefficient and aspect ratio. In the preform shape prediction, the simulated cylindrical radius shows good coincidence with the desired radius after forming. From this forward and inverse investigation, the ANFIS is proved to supply a useful optimal soft computing approach in the forming category. © 2003 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/158937
ISSN
2015 Impact Factor: 2.359
2015 SCImago Journal Rankings: 1.464
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLu, YHen_US
dc.contributor.authorYeh, FHen_US
dc.contributor.authorLi, CLen_US
dc.contributor.authorWu, MTen_US
dc.contributor.authorLiu, CHen_US
dc.date.accessioned2012-08-08T09:04:40Z-
dc.date.available2012-08-08T09:04:40Z-
dc.date.issued2003en_US
dc.identifier.citationJournal Of Materials Processing Technology, 2003, v. 140 n. 1-3 SPEC., p. 576-582en_US
dc.identifier.issn0924-0136en_US
dc.identifier.urihttp://hdl.handle.net/10722/158937-
dc.description.abstractThis paper combines adaptive-network-based inference system (ANFIS) and elasto-plastic finite element to predict the ductile fracture initiation and the preform shape of the upsetting process. From the hybrid-learning algorithm in ANFIS, it can efficiently construct rule database and optimal distribution of membership function to solve the punch stroke which causes the ductile fracture, and the preform shape which results a desired cylindrical workpiece after forming in the upsetting process. As a verification of this system, the punch stroke for ductile fracture initiation and the free boundary radius of the billet after forming are compared between ANFIS and FEM simulated results. In the ductile fracture prediction, it is proved that ANFIS can efficiently predict the ductile fracture initiation successfully for arbitrary friction coefficient and aspect ratio. In the preform shape prediction, the simulated cylindrical radius shows good coincidence with the desired radius after forming. From this forward and inverse investigation, the ANFIS is proved to supply a useful optimal soft computing approach in the forming category. © 2003 Elsevier B.V. All rights reserved.en_US
dc.languageengen_US
dc.publisherElsevier SA. The Journal's web site is located at http://www.elsevier.com/locate/jmatprotecen_US
dc.relation.ispartofJournal of Materials Processing Technologyen_US
dc.subjectAdaptive-Network-Based Inference Systemen_US
dc.subjectElasto-Plastic Finite Elementen_US
dc.subjectPreformen_US
dc.titleStudy of ductile fracture and preform design of upsetting process using adaptive network fuzzy inference systemen_US
dc.typeConference_Paperen_US
dc.identifier.emailLiu, CH:chliu@hkucc.hku.hken_US
dc.identifier.authorityLiu, CH=rp00152en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/S0924-0136(03)00795-7en_US
dc.identifier.scopuseid_2-s2.0-0042410786en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0042410786&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume140en_US
dc.identifier.issue1-3 SPEC.en_US
dc.identifier.spage576en_US
dc.identifier.epage582en_US
dc.identifier.isiWOS:000185489700100-
dc.publisher.placeSwitzerlanden_US
dc.identifier.scopusauthoridLu, YH=8665536000en_US
dc.identifier.scopusauthoridYeh, FH=7101747917en_US
dc.identifier.scopusauthoridLi, CL=7501676674en_US
dc.identifier.scopusauthoridWu, MT=7405593817en_US
dc.identifier.scopusauthoridLiu, CH=36065161300en_US

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