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Technical Brief

Continuous Monitoring of Metal Working Fluid Quality in Machining Processes

[+] Author and Article Information
Benjamin Glasse

Particles and Process Engineering,
University Bremen,
Badgasteiner Straße 3,
Bremen 28359, Germany

Udo Fritsching

Professor
Particles and Process Engineering,
University Bremen,
Badgasteiner Straße 3,
Bremen 28359, Germany;
Institute of Materials Science,
Badgasteiner Straße 3,
Bremen 28359, Germany
e-mail: ufri@iwt.uni-bremen.de

1Corresponding author.

Manuscript received November 2, 2015; final manuscript received September 23, 2016; published online October 18, 2016. Assoc. Editor: Laine Mears.

J. Manuf. Sci. Eng 139(4), 044501 (Oct 18, 2016) (5 pages) Paper No: MANU-15-1546; doi: 10.1115/1.4034889 History: Received November 02, 2015; Revised September 23, 2016

Metal working fluid (MWF) emulsions are utilized as coolants and lubricants in machining processes like turning or drilling. During their operation life time cycle, MWFs change their properties due to impacting stresses which may influence the machining and tool performance. A frequent refreshing or renewal of MWFs in machining process is thus necessary. This investigation discusses measurement techniques of MWF emulsions to be used for MWF quality assessment and process monitoring. By means of optical spectroscopic measurement techniques (turbidimetry and laser diffraction), the evaluation of the temporal change of the wavelength exponent and the MWF emulsion droplet size is related to the MWF stability. The in-process monitoring of the MWFs in machining during several weeks of operation is shown. Thus, it will be demonstrated that optical spectroscopic measurement techniques may be applied to determine stability change of the emulsion system.

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Figures

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Fig. 1

Temporal change of the wavelength exponent and the droplet size for different metal working machines (Table 2): machine 1 (a), machine 2 (b), machine 3 (c), and machine 4 (d)

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Fig. 2

Dependence of the wavelength exponent and coefficient of determination from droplet size d3,2 for metal working machine 2 (a) and machine 3 (b)

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Fig. 3

Temporal change of droplet size distributions (a), concentration and wavelength exponent (b) during the operational time of machine 5

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Fig. 4

Temporal change of the wavelength exponent for machine 6 during MWF maintenance

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Fig. 5

Temporal change of the retrieved droplet size d3,2 in dependence of the discretization n for (a) least square fit, (b) Chahine, (c) projected Landweber, and (d) NNTPR with a H1

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