Mоlimо vаs kоristitе оvај idеntifikаtоr zа citirаnjе ili оvај link dо оvе stаvkе: https://open.uns.ac.rs/handle/123456789/12049
Pоljе DC-аVrеdnоstЈеzik
dc.contributor.authorKovač P.en
dc.contributor.authorSavković, Borislaven
dc.contributor.authorRodić D.en
dc.contributor.authorAleksić A.en
dc.contributor.authorGostimirović, Marinen
dc.contributor.authorSekulić M.en
dc.contributor.authorKulundžić, Nenaden
dc.date.accessioned2020-03-03T14:46:58Z-
dc.date.available2020-03-03T14:46:58Z-
dc.date.issued2020-01-01en
dc.identifier.isbn9783030313425en
dc.identifier.issn21954356en
dc.identifier.urihttps://open.uns.ac.rs/handle/123456789/12049-
dc.description.abstract© 2020, Springer Nature Switzerland AG. The objective of this study is to examine the influence of machining parameters on surface finish in turning of medical steel. A new approach in modeling surface roughness which uses design of experiments is described in this paper. The values of surface roughness predicted by different models are then compared. Used were adaptive-neuro-fuzzy-inference system (ANFIS). The results showed that the proposed system can significantly increase the accuracy of the product profile when compared to the conventional approaches. The results indicate that the design of experiments with central composition plan modeling technique can be effectively used for the prediction of the surface roughness for medical steel difficult to machining. Optimizations of surface roughness parameters was done by use of ant colony method.en
dc.relation.ispartofLecture Notes in Mechanical Engineeringen
dc.titleModelling and Optimization of Surface Roughness Parameters of Stainless Steel by Artificial Intelligence Methodsen
dc.typeConference Paperen
dc.identifier.doi10.1007/978-3-030-31343-2_1en
dc.identifier.scopus2-s2.0-85076208076en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85076208076en
dc.relation.lastpage12en
dc.relation.firstpage3en
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartman za proizvodno mašinstvo-
crisitem.author.deptDepartman za proizvodno mašinstvo-
crisitem.author.deptDepartman za proizvodno mašinstvo-
crisitem.author.parentorgFakultet tehničkih nauka-
crisitem.author.parentorgFakultet tehničkih nauka-
crisitem.author.parentorgFakultet tehničkih nauka-
Nаlаzi sе u kоlеkciјаmа:FTN Publikacije/Publications
Prikаzаti јеdnоstаvаn zаpis stаvki

SCOPUSTM   
Nаvоđеnjа

2
prоvеrеnо 14.09.2022.

Prеglеd/i stаnicа

23
Prоtеklа nеdеljа
12
Prоtеkli mеsеc
0
prоvеrеnо 10.05.2024.

Google ScholarTM

Prоvеritе

Аlt mеtrikа


Stаvkе nа DSpace-u su zаštićеnе аutоrskim prаvimа, sа svim prаvimа zаdržаnim, оsim аkо nije drugačije naznačeno.