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Investigation on the effect of cutting fluid pressure on surface quality measurement in high speed thread milling of brass alloy (C3600) and aluminium alloy (5083)

Khorasani, Amir Mahyar, Gibson, Ian, Goldberg, Moshe, Doeven, Egan H. and Littlefair, Guy 2016, Investigation on the effect of cutting fluid pressure on surface quality measurement in high speed thread milling of brass alloy (C3600) and aluminium alloy (5083), Measurement: journal of the international measurement confederation, vol. 82, pp. 55-63, doi: 10.1016/j.measurement.2015.12.016.

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Title Investigation on the effect of cutting fluid pressure on surface quality measurement in high speed thread milling of brass alloy (C3600) and aluminium alloy (5083)
Author(s) Khorasani, Amir Mahyar
Gibson, IanORCID iD for Gibson, Ian orcid.org/0000-0002-4149-9122
Goldberg, Moshe
Doeven, Egan H.ORCID iD for Doeven, Egan H. orcid.org/0000-0003-2677-4269
Littlefair, Guy
Journal name Measurement: journal of the international measurement confederation
Volume number 82
Start page 55
End page 63
Total pages 9
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-03
ISSN 0263-2241
Keyword(s) Artificial neural networks
High speed machining
Modelling operational
Thread milling
Science & Technology
Technology
Engineering, Multidisciplinary
Instruments & Instrumentation
Engineering
TITANIUM-ALLOY
ROUGHNESS
MODEL
COMPOSITES
FORCES
TOOLS
Summary The quality of a machined finish plays a major role in the performance of milling operations, good surface quality can significantly improve fatigue strength, corrosion resistance, or creep behaviour as well as surface friction. In this study, the effect of cutting parameters and cutting fluid pressure on the quality measurement of the surface of the crest for threads milled during high speed milling operations has been scrutinised. Cutting fluid pressure, feed rate and spindle speed were the input parameters whilst minimising surface roughness on the crest of the thread was the target. The experimental study was designed using the Taguchi L32 array. Analysing and modelling the effective parameters were carried out using both a multi-layer perceptron (MLP) and radial basis function (RBF) artificial neural networks (ANNs). These were shown to be highly adept for such tasks. In this paper, the analysis of surface roughness at the crest of the thread in high speed thread milling using a high accuracy optical profile-meter is an original contribution to the literature. The experimental results demonstrated that the surface quality in the crest of the thread was improved by increasing cutting speed, feed rate ranging 0.41-0.45 m/min and cutting fluid pressure ranging 2-3.5 bars. These outcomes characterised the ANN as a promising application for surface profile modelling in precision machining.
Language eng
DOI 10.1016/j.measurement.2015.12.016
Field of Research 091399 Mechanical Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083531

Document type: Journal Article
Collections: School of Life and Environmental Sciences
School of Engineering
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