Publication:
Prediction of cutting force model by using neural network

Simple item page
dc.citedby9
dc.contributor.authorKadirgama K.en_US
dc.contributor.authorAbou-El-Hossein K.A.en_US
dc.contributor.authorid12761486500en_US
dc.contributor.authorid8367728100en_US
dc.date.accessioned2023-12-28T08:57:44Z
dc.date.available2023-12-28T08:57:44Z
dc.date.issued2006
dc.description.abstractThis study describes application of neural network methods to predict the cutting force model in milling 618 stainless steel. Cutting force was taken as response and the variables (cutting speed, feed rate, axial depth and radial depth). Design of experiments was used to reduce the number of the experiments and provide the optimum experiments condition. The predictive result between experimental result and neural network were compared. The error from the neural network prediction result was acceptable since the value of the prediction was closer to the experimental result. � 2006 Asian Network for Scientific Information.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.3923/jas.2006.31.34
dc.identifier.epage34
dc.identifier.issue1
dc.identifier.scopus2-s2.0-33644619567
dc.identifier.spage31
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-33644619567&doi=10.3923%2fjas.2006.31.34&partnerID=40&md5=9c83cc1a7287c4b8f8324688575cd0fc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29806
dc.identifier.volume6
dc.pagecount3
dc.relation.ispartofAll Open Access; Bronze Open Access
dc.sourceScopus
dc.sourcetitleJournal of Applied Sciences
dc.subjectCutting force
dc.subjectMilling design of experiments
dc.subjectNeural network
dc.subjectCutting
dc.subjectDesign of experiments
dc.subjectExperiments
dc.subjectForecasting
dc.subjectMilling (machining)
dc.subjectCutting force model
dc.subjectCutting forces
dc.subjectCutting speed
dc.subjectFeed-rates
dc.subjectNeural network method
dc.subjectNeural network predictions
dc.subjectNeural networks
dc.titlePrediction of cutting force model by using neural networken_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS