Optimized characterization of response surface methodology on lubricant with titanium oxide nanoparticles

Abstract

Purpose An optimized model is often deployed to reduce trial and error in experimental approach and obtain the multi-variant correlation. In this study, response surface methodology (RSM), namely, Box–Behnken design (BBD) approach, has been used to optimize the characterization of lubricant with additives. BBD is based on multivariate analysis whereby the effects of different parameters are considered simultaneously. It is a non-linear system which is more representative of the actual phenomenon. This study aims to investigate the effect of three independent variables, namely, speed, load and concentration of TiO2, on the coefficient of friction (CoF). Design/methodology/approach RSM was applied to get the multiplicity of the self-determining input variables and construct mathematical models. Mathematical models were established to predict the CoF and to conduct a statistical analysis of the independent variables’ interactions on response surface using Minitab 16.0 statistical software. Three parameters were regulated: speed (X1), load (X2) and concentration of TiO2 (X3). The output measured was the CoF. Findings The result obtained from BBD has shown that the most influential parameter was speed, followed by concentration of TiO2 nanoparticles and then normal load. Analysis of variance indicated that the proposed experiment from the quadratic model has successfully interpreted the experimental data with a coefficient of determination R2 = 0.9931. From the contour plot of BBD, the optimization zone for interacting variables has been obtained. The zone indicates two regions of lower friction values (<0.04): concentration between 0.5 to 1.0 Wt.% for a speed range of 1,000 to 2,000 rpm, and load between 17 to 20 kg for a speed in the range of 1,200 to 1,900 rpm. The optimized condition shows that the minimum value of CoF (0.0191) is at speed of 1,782 rpm, load of 20 kg and TiO2 concentration of 1.0 Wt.%. Originality/value In general, it has been shown that RSM is an effective and powerful tool in experimental optimization of multi-variants.