This work studies the effect of minimum quantity lubrication (MQL) using Nano alumina mixed coconut oil-based cutting fluid for the machining performance of SS304.The machining characteristics such as the material removal rate (MRR) and surface roughness (SR) are examined. An attempt is made by using coconut oil mixed along with various Wt% (0, 2, 4,6) of Nano aluminum oxide particles .The machining parameters ranges are Cutting Speed of 1200-1800 rpm, Feed Rate of 0.16-0.22 mm/rev and Depth of Cut of 0.5- 2mm respectively. MRR and SR of the machined surfaces are modified by the Alumina particles in the coconut oil alumina based cutting fluid. It is witnessed that MQL (Coconut oil +6% of Al2O3 nano powder) performs better than MQL (coconut oil). MQL (Coconut oil + Al2O3 nano powder) reveals the higher material removal rate, reduced the surface roughness and better surface finish than MQL (coconut oil).
Biomass, noted for its adaptability, has various applications in biofuel generation, industrial use, and environmental cleaning. This study looks into the multiple roles of biomass as a renewable energy source, with a particular emphasis on its vital contribution to biofuel production. Through a thorough evaluation of different conversion routes—thermal, biological, and physical—the study emphasizes thermochemical processes' efficiency, cost-effectiveness, and adaptability. Notably, technologies like gasification and quick pyrolysis are thoroughly investigated, followed by in-depth discussions of reactor optimization strategies to enhance performance and output. The complex structure of biomass, which is dominated by high-molecular-weight polysaccharides such as cellulose and hemicelluloses, demonstrates its significant potential for energy generation. Furthermore, the study categorizes biomass by content, origin, and conversion processes, resulting in a comprehensive inventory of available resources. Biomass from the agriculture and forestry industries, such as starch, sugar, lignocellulose, and organic wastes, is rigorously analyzed for energy production. Furthermore, various biomass processing techniques, including thermochemical, biochemical, and physicochemical conversions, are carefully tested in real-world applications to ensure their efficacy and viability. Beyond its importance in biofuel production, the article underlines biomass' versatility in satisfying industrial needs and contributing to environmental cleanup initiatives. This study lays the groundwork for informed decision-making and innovative solutions in various industries by providing a thorough understanding of biomass's various benefits and applications, including energy provision, industrial processes, and ecological restoration.
In the modern era, manufacturers aim for their parts to possess a sleek finish and increased durability to ensure continued functionality. Both the automotive and aerospace industries are actively seeking new materials and methodologies to enhance the surface quality of components during the preparation process and make the most of available resources. Employing casting, advanced techniques, and new materials is crucial for achieving this goal. Industries commonly utilize alloys and composite materials in the production of their components. This study focuses on magnesium composite Mg-4Zn-1RE-0.7Zr alloy to find the influence of varying three different reinforcement particles on mechanical properties and wear rate. An attempt was made to choose a constant 5% Si 3 N 4 as the primary reinforcement and 2.5% to 7.5% of TiC/MoS 2 as the secondary reinforcement, respectively. The samples of magnesium hybrid composites are prepared using a centrifugal casting process. The ZE 41 alloy/5% (TiC-MoS 2 )/5%Si 3 N 4 has a high tensile strength of 942 MPa. In addition, 5% TiC/5% MoS 2 with 5% Si 3 N 4 composite has enhanced hardness, which is beneficial for transmission in aircraft like Boeing 727 and die-casting fittings in automobile applications.
This study looks at how incorporating nanofiller into sisal/flax-fibre-reinforced epoxy-based hybrid composites affects their mechanical and water absorption properties. The green Al2O3 NPs are generated from neem leaves in a proportion of leaf extract to an acceptable aluminium nitrate combination. Both natural fibres were treated with different proportions of NaOH to eliminate moisture absorption. The following parameters were chosen as essential to achieving the objectives mentioned above: (i) 0, 5, 10, and 15% natural fibre concentrations; (ii) 0, 2, 4, and 6% aluminium powder concentrations; and (iii) 0, 1, 3, and 5% NaOH concentrations. Compression moulding was used to create the hybrid nanocomposites and ASTM standards were used for mechanical testing such as tension, bending, and impact. The findings reveal that combining sisal/flax fibre composites with nanofiller improved the mechanical features of the nanocomposite. The sisal and flax fibre hybridised successfully, with 10% fibres and 4% aluminium filler. The water absorption of the hybrids rose as the fibre weight % increased, and during the next 60 h, all of the specimens achieved equilibrium. The failed samples were examined using scanning electron Microscopic (SEM) images better to understand the composite’s failure in the mechanical experimentations. Al2O3 NPs were confirmed through XRD, UV spectroscope and HPLC analysis. According to the HPLC results, the leaf’s overall concentrations of flavonoids (gallocatechin, carnosic acid, and camellia) are determined to be 0.250 mg/g, 0.264 mg/g, and 0.552 mg/g, respectively. The catechin concentration is higher than the phenolic and caffeic acid levels, which could have resulted in a faster rate of reduction among many of the varying configurations, 4 wt.% nano Al2O3 particle, 10 wt.% flax and sisal fibres, as well as 4 h of NaOH with a 5 wt.% concentration, producing the maximum mechanical properties (59.94 MPa tension, 149.52 Mpa bending, and 37.9 KJ/m2 impact resistance). According to the results, it can be concluded that botanical nutrients may be used effectively in the manufacturing of nanomaterials, which might be used in various therapeutic and nanoscale applications.
Abstract The present investigation focuses on the fabrication of Copper-High Entropy Alloy (HEA) surface Metal Matrix Composite (MMC) using the solid-state Friction Stir Process (FSP) and the characterization of wear characteristics. Higher hardness values at the level of 770HV were the cornerstone in its selection, in addition to identifying several appropriate considerations for combining the AlCoCrCuFe HEA in Cu-HEA surface MMCs. Because of the combination of FSP and HEA, the produced composite had a fine microstructure and increased hardness. The wear test is carried out using pin-on-disc equipment for all conceivable parameter combinations to thoroughly analyze wear qualities, with velocity, load, as well as sliding distance chosen as input parameters. The wear rate decreases dramatically with HEA additions and rises with sliding velocity, load, and sliding distance. The impact of HEA addition on the Coefficient of Friction (CoF) during a dry sliding wear test is opposed to its influence on wear rate. The wear parameters such as load, sliding speed, and sliding distance possess a positive correlation with the wear rate and a negative correlation with a coefficient of friction. The applied load has a severe effect on wear rate and CoF when compared to other wear parameters considered. Scanning Electron Microscope (SEM) micrographs of the worn surface were utilized to analyze the wear process, which clearly showed that the copper’s wear resistance improved with the addition of HEA.
Biomass is an excellent source of green energy with numerous benefits such as abundant availability, net carbon zero, and renewable nature. However, the conventional methods of biomass combustion are polluting and poor efficiency processes. Biomass gasification overcomes these challenges and provides a sustainable method for the supply of greener fuel in the form of producer gas. The producer gas can be employed as a gaseous fuel in compression ignition engines in dual‐fuel systems. The biomass gasification process is a complex as well as a nonlinear process that is highly dependent on the ambient environment, type of biomass, and biomass composition as well as the gasification medium. This makes the modeling of such systems quite difficult and time‐consuming. Modern machine learning (ML) techniques offer the use of experimental data as a convenient approach to modeling and forecasting such systems. In the present study, two modern and highly efficient ML techniques, random forest (RF) and AdaBoost, were employed for this purpose. The outcomes were employed with results of a baseline method, i.e., linear regression. The RF could forecast the hydrogen yield with R 2 as 0.978 during model training and 0.998 during the model test phase. AdaBoost ML was close behind with R 2 at 0.948 during model training and 0.842 during the model test phase. The mean squared error was as low as 0.17 and 0.181 during model training and testing, respectively. In the case of the low heating value model, during model testing, the R 2 was 0.971 and RF and AdaBoost, respectively, during model training and 0.842 during the model test phase. Both ML techniques provided excellent results compared to linear regression, but RFt was the best among all three.
Electrochemical micromachining (ECMM) finds application in various industries especially in surface finishing process in aerospace industries. In this research the workpiece made from aluminum scrap metal matrix reinforced with alumina is subjected to wear, surface profile and machinability studies. To analysis the ECMM performance simple additive weighting (SAW) CRiteria Importance Through Intercriteria Correlation (CRITIC) and Artificial Neural Network (ANN) was used. The wear studies show that at high loads the height wear loss is less and frictional force is more. The L18 mixed orthogonal array experiments was conducted and analysis of experiments shows that the most crucial parameter values for high MRR and low OC are 28g/lit NaNO3+0.05M HNO3, 10 V, and 80% duty cycle. The weight values of the performance metrics obtained using SAW method are 0.549 and 0.45. The optimal output performance predicted by ANN is MRR of 0.520 ?m/sec and OC of 23.8 ?m.
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