Simultaneous Determination of Droplet Size, pH Value and Concentration to Evaluate the Aging Behavior of Metalworking Fluids

Metalworking fluids (MWFs) are widely used to cool and lubricate metal workpieces during processing to reduce heat and friction. Extending a MWF’s service life is of importance from both economical and ecological points of view. Knowledge about the effects of processing conditions on the aging behavior and reliable analytical procedures are required to properly characterize the aging phenomena. While so far no quantitative estimations of ageing effects on MWFs have been described in the literature other than univariate ones based on single parameter measurements, in the present study we present a simple spectroscopy-based set-up for the simultaneous monitoring of three quality parameters of MWF and a mathematical model relating them to the most influential process factors relevant during use. For this purpose, the effects of MWF concentration, pH and nitrite concentration on the droplet size during aging were investigated by means of a response surface modelling approach. Systematically varied model MWF fluids were characterized using simultaneous measurements of absorption coefficients µ(a) and effective scattering coefficients µ’(s). Droplet size was determined via dynamic light scattering (DLS) measurements. Droplet size showed non-linear dependence on MWF concentration and pH, but the nitrite concentration had no significant effect. pH and MWF concentration showed a strong synergistic effect, which indicates that MWF aging is a rather complex process. The observed effects were similar for the DLS and the µ’(s) values, which shows the comparability of the methodologies. The correlations of the methods were R(2)(c) = 0.928 and R(2)(P) = 0.927, as calculated by a partial least squares regression (PLS-R) model. Furthermore, using µ(a), it was possible to generate a predictive PLS-R model for MWF concentration (R(2)(c) = 0.890, R(2)(P) = 0.924). Simultaneous determination of the pH based on the µ’(s) is possible with good accuracy (R²(c) = 0.803, R²(P) = 0.732). With prior knowledge of the MWF concentration using the µ(a)-PLS-R model, the predictive capability of the µ’(s)-PLS-R model for pH was refined (10 wt%: R²(c) = 0.998, R²(p) = 0.997). This highlights the relevance of the combined measurement of µ(a) and µ’(s). Recognizing the synergistic nature of the effects of MWF concentration and pH on the droplet size is an important prerequisite for extending the service life of an MWF in the metalworking industry. The presented method can be applied as an in-process analytical tool that allows one to compensate for ageing effects during use of the MWF by taking appropriate corrective measures, such as pH correction or adjustment of concentration.