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Solid State PhenomenaVolume 261 SSP, 2017, Pages 277-2849th International Congress on Precision Machining, ICPM 2017; Athens; Greece; 6 September 2017 through 9 September 2017; Code 196839

Application of neuro-fuzzy systems for modeling surface roughness parameters for difficult-to-cut-steel(Conference Paper)

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  • aUniversity of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovića 6, Novi Sad, 21000, Yugoslavia
  • bInternational Technology Management Academy, Trg Dositeja Obradovića 7, Novi Sad, 21000, Yugoslavia

Abstract

The objective of this study is to examine the influence of machining parameters on surface finish in turning difficult-to-cut-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. Adaptive-neuro-fuzzy-inference system (ANFIS) was used. 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 difficult-to-cut-steel.

Author keywords

ANFIS modelingDifficult to machiningSurface roughnessTurning

Indexed keywords

Engineering controlled terms:Design of experimentsFuzzy inferenceFuzzy neural networksFuzzy systemsTurning
Engineering uncontrolled termsAdaptive neuro-fuzzy inferenceAdaptive-neuro-fuzzy-inference system modelingDifficult to machiningMachining parametersModel surfaceNeuro-fuzzy inference systemsNeurofuzzy systemSurface finishesSurface roughness parametersSystem models
Engineering main heading:Surface roughness

Funding details

Funding sponsor Funding number Acronym
Ministarstvo Prosvete, Nauke i Tehnološkog RazvojaMPNTR

  • 1

    The paper is the result of the research within the project TR 35015 financed by the ministry of science and technological development of the Republic of Serbia and CEEPUS project.

  • ISSN: 10120394
  • ISBN: 978-303571199-8
  • Source Type: Book Series
  • Original language: English
  • DOI: 10.4028/www.scientific.net/ssp.261.277
  • Document Type: Conference Paper
  • Volume Editors: Markopoulos A.P.,Vosniakos G.-C.
  • Publisher: Trans Tech Publications Ltd

  Kovač, P.; University of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovića 6, Novi Sad, Yugoslavia;
© Copyright 2024 Elsevier B.V., All rights reserved.

Cited by 1 document

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(2018) Materials Science Forum
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