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)

Amir Mahyar Khorasani*, Ian Gibson, Moshe Goldberg, Egan H. Doeven, Guy Littlefair

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    13 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)55-63
    Number of pages9
    JournalMeasurement: Journal of the International Measurement Confederation
    Volume82
    DOIs
    Publication statusPublished - 1 Mar 2016

    Keywords

    • Artificial neural networks
    • High speed machining
    • Modelling operational
    • Thread milling

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