CHARACTERIZATION OF METAL WORKING FLUIDS USING TRIBOLOGICAL TESTING METHODS

Abstract

The objective of this paper is to determine whether it is possible to compare the lubricating properties of different Metal working fluids (MWF) for a defined cutting application using easily reproducible tribological tests. The idea is that this will eventually allow users of MWF to select the most suitable MWF for a specific application without the need to run expensive and time-consuming tests on a machine tool being unavailable for production during that time. The test method chosen is the Pin-on-Disc (PoD) test, as there has been extensive research and an easy setup can be acquired inexpensively, making it more available and attractive for potential users. The PoD-tests were done with two groups of MWF, those for wet machining and those for minimum quantity lubrication (MQL). Additionally, the viscosity of the MQL-MWF has been measured using a capillary viscometer. The evaluation of the wear on the pins and discs with different measuring devices shows consistent and promising results for the comparison of MQL-MWF, while the MWF for wet machining display only limited comparability with the PoD-setup used.

Recommended articles

INCREASING PERFORMANCE AND ENERGY EFFICENCY OF A MACHINE TOOL THROUGH HYDROSTATIC LINEAR GUIDEWAYS WITH SINGLE DIGIT MICROMETRE FLUID FILM THICKNESS

M. Fritz, M. Groeb
Keywords: hydrostatic | guideway | Machine tool | precision | microgap | Energy Efficiency

USING GAMIFICATION ON THE SHOP FLOOR FOR PROCESS OPTIMIZATION IN MACHINING PRODUCTION

M. Dewald, O. Kohn, Y. Dehorn, H. Howaldt, A. Ebben, N. Kratzke, F. Janssen, M. Weigold
Keywords: Process optimization | Internet of Things | Machine tools | Gamification

CRYOGENIC MILLING OF METASTABLE AUSTENITIC STAINLESS STEEL AISI 347

K. Gutzeit, S. Basten, B. Kirsch, J.C. Aurich
Keywords: Cryogenic milling | Surface morphology | Deformation induced phase transformation

MACHINE DATA-BASED PREDICTION OF BLISK BLADE GEOMETRY CHARACTERISTICS

A. Ernst, M. Weigold
Keywords: quality | Machine Learning | milling | Aviation Industry