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Journal of Intelligent Manufacturing

Erschienen in:

23.04.2014

Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling

verfasst von: Emel Kuram, Babur Ozcelik

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 4/2016

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Abstract

In this study, micro-milling of AISI 304 stainless steel with ball nose end mill was conducted using Taguchi method. The influences of spindle speed, feed rate and depth of cut on tool wear, cutting forces and surface roughness were examined. Taguchi’s signal to noise ratio was utilized to optimize the output responses. The influence of control parameters on output responses was determined by analysis of variance. In this study, the models describing the relationship between the independent variables and the dependent variables were also established by using regression and fuzzy logic. Efficiency of both models was determined by analyzing correlation coefficients and by comparing with experimental values. The results showed that both regression and fuzzy logic modelling could be efficiently utilized for the prediction of tool wear, cutting forces and surface roughness in micro-milling of AISI 304 stainless steel.

Vorheriger Artikel Risk evaluation in failure mode and effects analysis using fuzzy digraph and matrix approach

Nächster Artikel An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems

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Titel: Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling
verfasst von: Emel Kuram
Babur Ozcelik
Publikationsdatum: 23.04.2014
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 4/2016
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-014-0916-5

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