This review article summarizes the progress of research on carbon nanomaterial-based lubricants witnessed in recent years. Carbon nanomaterials, such as graphene, carbon nanotubes (CNTs), fullerenes and carbon nanostructures, are at the center of current tribological research on attaining superior lubrication performance. The development of nanomaterial-based solid lubricants, lubricant additives and bulk materials and the related issues in their processing, characterization and applications as well as their tribological performance (coefficient of friction and wear rate) are listed in a structured tabulated form. Firstly, regarding nanomaterial-based solid lubricants, this study reveals that carbon nanomaterials such as graphite, graphene, graphene-based coatings and diamond-like carbon (DLC)-based coatings increase different tribological properties of solid lubricants. Secondly, this study summarizes the influence of graphene, carbon nanotubes, fullerene, carbon nanodiamonds, carbon nano-onions, carbon nanohorns and carbon spheres when they are used as an additive in lubricants. Thirdly, a structured tabulated overview is presented for the use of carbon nanomaterial-reinforced bulk material as lubricants, where graphene, carbon nanotubes and carbon nanodiamonds are used as reinforcement. Additionally, the lubricity mechanism and superlubricity of carbon nanomaterial-based lubricants is also discussed. The impact of carbon nanotubes and graphene on superlubricity is reviewed in detail. It is reported in the literature that graphene is the most prominent and widely used carbon nanomaterial in terms of all four regimes (solid lubricants, lubricating additives, bulk material reinforcement and superlubricity) for superior tribological properties. Furthermore, prospective challenges associated with lubricants based on carbon nanomaterials are identified along with future research directions.

The effect of four controllable input process parameters of AISI 4140 steel, cross-feed, workpiece velocity, wheel velocity, and the depth of cut were experimentally investigated under dry and wet conditions. Three responses, contact temperature, material removal rate (MRR), and machining cost during surface grinding of AISI 4140 steel, were considered. The process was optimized using a recently developed combined methodology based on response surface methodology (RSM) and desirability functional approach (DFA). RSM generated the models of the responses for prediction while DFA solved these multi-response optimization problems. The DFA approach employed an objective function known as the desirability function, which converts an estimated response into a scale-free value known as desirability. The optimum parameter was attained at the maximum overall desirability. An analysis of variance (ANOVA) was conducted to confirm the model adequacy. From the results of the study, for equal weights of responses, the corresponding optimal values of the input parameters cross-feed, workpiece velocity, the wheel or cutting velocity and the depth of cut were found to be 6 mm/pass, 12 m/min, 15 m/s, and 0.095 mm respectively in wet conditions. The corresponding predicted output responses were: 134.55 °C for the temperature, and 7.366 BDT (Taka, Currency of Bangladesh) for the total cost with an overall desirability of 0.844. Confirmation testing of optimized parameters, i.e., checking the validity of optimal set of predicted responses with the real experimental run were conducted, and it was found that the experimental value for temperature and total cost were 140.854 °C and 8.36 BDT, respectively, with an overall desirability of 0.863. Errors of the predicted value from the experimental value for equal weightage scheme were 4.47% for the temperature and 7.37% for the total cost. It was also found that if the temperature was prioritized, then the wet condition dominated the overall desirability, which was expected. However, if the cost was given high weightage, dry condition achieved the highest overall desirability. This can be attributed to the cutting in the wet condition which was more expensive due to the application of cutting fluid. The proposed model was found to be new and highly flexible in the sense that there was always an option at hand to focus on a particular response if needed.

This research addresses the challenge of determining the optimal parameters for the selective laser sintering (SLS) process using thermoplastic polyurethane elastomer (TPU) flexa black powder to achieve high-quality SLS parts. This study focuses on two key printing process parameters, namely layer thickness and the laser power ratio, and evaluates their impact on four output responses: density, hardness, modulus of elasticity, and time required to produce the parts. The primary impacts and correlations of the input factors on the output responses are evaluated using response surface methodology (RSM). A particular response optimizer is used to find the optimal settings of input variables. Additionally, the rationality of the model is verified through an analysis of variance (ANOVA). The research identifies the optimal combination of process parameters as follows: a 0.11 mm layer thickness and a 1.00 laser power ratio. The corresponding predicted values of the four responses are 152.63 min, 96.96 Shore-A, 2.09 MPa, and 1.12 g/cm³ for printing time, hardness, modulus of elasticity, and density, respectively. These responses demonstrate a compatibility of 66.70% with the objective function. An experimental validation of the predicted values was conducted and the actual values obtained for printing time, hardness, modulus of elasticity, and density at the predicted input process parameters are 159.837 min, 100 Shore-A, 2.17 MPa, and 1.153 g/cm³, respectively. The errors between the predicted and experimental values for each response (time, hardness, modulus of elasticity, and density) were found to be 4.51%, 3.04%, 3.69%, and 2.69%, respectively. These errors are all below 5%, indicating the adequacy of the model. This study also comprehensively describes the influence of process parameters on the responses, which can be helpful for researchers and industry practitioners in setting process parameters of similar SLS operations.

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.

The recent enormous expansion of the electronic industry and the extensive use of digital devices in this Industry 4.0 era have affected e-waste management significantly, particularly for emerging economies. Implementing artificial intelligence (AI) based technologies is imperative to modernize conventional e-waste management systems and guarantee commercial viability and environmental sustainability. Therefore, this study aims to discover, evaluate, and rank the I4.0-based AI-improved for sustainable e-waste management systems. A blended approach consisting of the Delphi and Best-Worst Method (BWM) was combined to satisfy the objectives. Based on the experts' suggestions and extensive literature review, nine I4.0-based AI imperatives were screened out and prioritized using BWM to evaluate their relative importance. The result uncovered that “Sustainability-Centered Product Design to Minimize E-Waste” is the most applicable I4.0-based imperative, followed by “Blockchain and IoT Enabled Intelligent E-Waste Collection and Traceability” for smartly improving the e-waste management system. The insights of this research can be helpful for the industry's top executives, practitioners, and other stakeholders in dealing with e-waste management in an intelligent and long-lasting manner in this Industry 4.0 era.