Electrically as well as thermally conductive lubricants have drawn considerable attention and are an emerging research topic because they have unique advantages and advanced lubrication performance over traditional lubricants such as corrosion protection and efficient heat dissipation. For instance, some components of electric vehicles (EVs) such as bearings, seals, pads and gears require conductive lubricants to avoid premature failure and electromagnetic interference (EMI) problems due to induced shaft voltages and currents. This review provides a comprehensive overview of the recent developments in conductive lubricants. The review focuses on the important aspects to enhance the thermal and electrical conductivities as well as the tribological behavior (COF, and wear rate) of conductive solid, semisolid, and liquid lubricants. The lubricants that are electrically and thermally conductive with superior tribological performances have been identified through extensive literature review and presented in tabular form. This review summarizes the effect of various additives used to improve the conductive properties of the lubricants, such as polyalphaolefin oil, hydraulic oil, paraffin oil, and mineral oil. Furthermore, the review discusses the lubricating mechanism of conductive solid and liquid lubricants to facilitate a deeper understanding. Finally, the future perspectives and the research directions for conductive lubricants are also addressed.

This study investigates and optimizes the machining parameters of the CNC end milling process applied to mild steel workpieces, with a focus on surface roughness measurements. Traditional approaches typically assess surface roughness post-machining, but this research employs Response Surface Methodology (RSM) to analyze the impact of critical milling parameters—spindle speed (N), depth of cut (d), and feed rate (f)—on surface finish. The experimental work was conducted using Minitab 18 software, and response optimization techniques were utilized through Regression Analysis (RA) to determine the optimal process parameters. The optimized parameters identified include a spindle speed of 2000 m/min, a feed rate of 700 mm/pass, and a depth of cut of 0.1 mm, which achieved a minimum surface roughness of 1.64256 μm. The proposed model demonstrated an accuracy of over 80% in fitting the investigated values, indicating its effectiveness in identifying optimal conditions and enhancing milling process efficiency.

The article addresses the development and evaluation of a novel environmentally-friendly cutting fluid prepared using sunflower oil and ZnO nanoparticles, specifically developed for use in metal machining processes. An experimental investigation was conducted to examine the impact of three adjustable input process factors: spindle speed, feed rate, and depth of cut on Mild steel. The investigation was carried out under two conditions: one with a 1%(weight) ZnO nano cutting fluid and the other with a 1.5%(weight) ZnO nano cutting fluid. The study focused on two factors: surface roughness and material removal rate (MRR), which were measured during the process of face-milling mild steel. The research aims to optimize the milling machine settings to improve the efficiency of machining mild steel as the work material. This will be achieved by using Response Surface Methodology (RSM). The ZnO nano-cutting fluid was created using a meticulous technique and its stability was assessed by several investigations. An investigation indicates that a ZnO nano-cutting fluid with a weight concentration of 1.5% is more stable. The process was enhanced by the use of a newly created integrated approach grounded on response surface methodology (RSM). The suitability of the model was confirmed by an analysis of variance (ANOVA). The analysis revealed that, when considering equal weights of responses, the ideal values for the input parameters of spindle speed, feed rate, and depth of cut were determined to be 408.081 rpm, 146.667 m/min, and 0.4878 mm accordingly, under the circumstances of using 1.5% weight of ZnO nano cutting fluid. The projected output responses were as follows: 777.920 for the MRR (Material Removal Rate) and 3.5297 for surface roughness. The composite attractiveness was calculated to be 0.61433. The suggested model in this research shows a fitting of over 80% with our studied values. This model may be used to discover the ideal states and enhance the efficiency of the milling process. The enhanced milling parameters exhibit the potential to enhance machining performance, offering useful insights for firms seeking environmentally sustainable alternatives for metal-cutting operations. This work contributes to the advancement of sustainable machining practices by developing a novel formulation for cutting fluid and providing a systematic approach to improving machining parameters.