The Use of D-optimal Design for Modeling and Analyzing the Tribological Characteristics of Journal Bearing Materials Lubricated by Nano-Based Biolubricants

Abstract

The Response Surface Methodology (RSM) based on D-optimal design was employed to investigate the tribological characteristics of journal bearing materials such as brass, bronze and copper lubricated by a biolubricant, Chemically Modified Rapeseed Oil (CMRO). The wear and friction performances were observed for the bearing materials tested with TiO 2 , WS 2 and CuO nano additives dispersed in the CMRO. The tests were performed by selecting sliding speed and load as numerical factor, and nano based biolubricant/bearing materials as the categorical factor to evaluate the tribological characteristics such as coefficient of friction (COF) and specific wear rate. The results showed that RSM based on D-optimal design was instrumental in the selection of suitable journal bearing materials for a typical system especially lubricated by nano based biolubricant. At a sliding speed of 2.0 m/s, load of 100 N, the bronze bearing material with Downloaded by [University of Sussex Library] at 06:53 12 August 2015 ACCEPTED MANUSCRIPT ACCEPTED MANUSCRIPT 2 CMRO containing CuO nanoparticles give lowest COF and wear rate. In addition, ScanningElectron Microscopy (SEM) examination of the worn bearing surfaces showed that the bronze bearing material lubricated with CMRO containing CuO nanoadditive is smoother than copper/brass bearing material.

Vegetable Oil based Biodegradable Refrigeration Oil - An Optimization of Tribological Characteristics Using D-optimal Design

“…The range of predicted value of the independent variables at the design points to the average prediction error evaluated the adequate precision ("Adeq Precision"). The signal to noise ratio (Adeq Precision), greater than 4 for both COF and SWR confirms the desirability of the model [26]. The "Lack of fit F-value" of 1.98 for COF and 1.60 for SWR implies the not significant relative to the pure error.…”

“…RSM can be applied to determine the interaction of several affecting factors [30]. A D-optimal test matrix based on RSM is employed for experimental design for the tribological test and mathematical modelling and also for the optimization of the output responsescoefficient of friction (COF) and specific wear rate (SWR) [31]. The experiment was designed by selecting the load and sliding velocity as a function of rotational speed as numerical factor and refrigeration oil as categorical factor to examine the tribological behaviour.…”

“…RSM is a collection of statistical and mathematical techniques useful for developing, improving, and optimizing processes. The objectives of quality improvement, including reduction invariability and improved process and product performance, can often be accomplished directly using RSM [26]. D-optimal designs are straight optimization techniques mostly suitable when classical designs do not apply and successful method for mixed type mixed-level factorial experimentations.…”

Effects of Nanoparticle Enhanced Lubricant Films in Thermal Design of Plain Journal Bearings at High Reynolds Numbers

“…As can be seen in the abovementioned articles, there has been numerous studies in literature to improve the performance and design of journal bearings by investigating the effect of different parameters [13]. In particular, the effect of additive nanoparticles is one of main research directions [14].…”

A Friction–Wear Correlation for Four-Ball Extreme Pressure Lubrication

“…asperity contact model to address the roughness effect in lubricated contact [8], finite element method to solve the lubricated contact problem involving complex domain of surface contacts [9,13], and molecular dynamics simulation to investigate tribological phenomena in multiscale modelling [14,15]. Other than Reynolds Equation and its variants, other lubrication models in the literature revolve around regression analysis based on statistical approaches [16][17][18][19], empirical model driven by experimental observations [20,21], and models derived from first principles [22][23][24].…”