This study used ultrasonic aided stir casting to cast composite with aluminum alloy, and the prepared composite materials were characterized by mechanical and microstructural analysis. In this study, micron-sized ceramic particle titanium carbide (TiC) in distinct ratios (0, 3, 6, 9, 12, 15, and 18 wt.%) along with 1 wt.% of montmorillonite nanoclay (MN) were reinforced into the matrix of AA1100 alloy. Microstructural analysis shows that the MN and TiC particles are evenly distributed inside the matrix of AA1100, as seen from scanning electron microscopy (SEM) images. Higher porosity is achieved for AA1100 + 1wt.%MN + 18wt.%TiC, which is 43.33% higher than the AA1100 + 1wt.%MN + 15wt.%TiC composite. When compared to the base alloy, the micro-hardness, ultimate tensile strength, flexural and impact strength of the aluminum MMCs increased dramatically with an increase in TiC percentage until 15wt.% of TiC, whereas 18wt.% TiC addition lowers the tensile, flexural, and impact strength by 2.97, 3.54, and 10.33%, respectively, than the addition of 15wt.%TiC. The wear rate of the composite is reduced with the inclusion of solid lubricant MN. With the addition of MN with 3, 6, 9, 12, and 15 wt% TiC, the composite shows a further reduction in wear rate of 41.66, 50, 53.57, 55.95, and 59.52%, respectively, compared to the base alloy. The i corr of AA1100 + 1wt.%MN + 15wt.%TiC composite is four times lower than the base alloy, revealing superior corrosion resistance compared to other composites and base alloy. Orowan strengthening mechanism and abrasion and adhesive type wear mechanism are identified as predominant mechanisms.

This work explores the optimization of drilling parameters in hybrid epoxy composites fortified with bamboo, sisal, and glass fibers that use Al2O3 nanofillers towards reducing surface roughness. Taguchi method using L16 orthogonal array was adopted to optimize the drilling process, and the composites were constructed by hand lay-up approach. Surface roughness was the output. The main input factors for optimization were point angle, feed rate, and cutting speed. The most effective drilling parameters were determined to be C4 (point angle), B1 (feed rate), and A4 (cutting speed) for reducing surface roughness based on the signal-to-noise (S/N) ratio. The ANOVA results reveal that 39.46% cutting speed and 30.20% feed rate have a considerable effect on the surface roughness, while point angle contributes 25.84% of the total effect. The model under investigation is appropriate, according to the ANOVA results, which have an estimated S value of 0.4447 and R2 value of 95.50%. Also, the confirmative results show that the surface roughness values guessed for the best cutting conditions are pretty close to what was found in the experiments and what was expected, with a difference of 2.40%. The results offer insights into effective drilling techniques for hybrid composite materials. The ideal settings can help producers achieve excellent surface quality while machining.

This study presents a novel method for predicting the performance and emissions of a Homogeneous Charge Compression Ignition-Direct Injection (HCCI-DI) engine fuelled by waste cooking oil biodiesel (WCOB) blended with Aluminum oxide (Al2O3) and Ferric chloride (FeCl3) nano additives and premixed with gasoline. The test fuels considered were pure diesel, WCOB, B50 (a 50-50 blend of diesel and WCOB), and the nano-additives at concentrations of 50 ppm and 100 ppm. All experiments were performed on a 4.4 kW HCCI-DI engine operating at 1500 rpm. The results revealed that, utilization of WCOB showed a substantial reduction in emissions. Hydrocarbon (HC), carbon monoxide (CO) and smoke emissions decreased by 54.17 %, 50 %, and 22.69 %. Nevertheless, oxides of nitrogen (NOx) emissions increased by 18.32 %. When introducing gasoline (20 %) as a premix in HCCI-DI engines, a favourable shift in emission and efficiency metrics was observed. The brake thermal efficiency (BTE) increased 4.23 %. Moreover, NOx and smoke emissions decreased by 4.3 % and 39.21 %. Furthermore, compared to conventional diesel-fueled combustion, the integration of the nano additives manifested promising results. After introducing Al2O3 into neat fuel, The BTE improved by 11.27 % for direct injection (DI) combustion and 18.31 % for HCCI-DI combustion. Additionally, there was a marked decrease in exhaust emissions. FeCl3 nano additive into the test fuel significantly reduced HC, CO, and smoke emissions. Furthermore, the random forest machine learning approach demonstrated an extensive accuracy in forecasting both engine performance and emissions for the HCCI-DI engine. The innovative combination of cutting-edge machine learning techniques and combustion technology used. This model to understand the complicated correlations between input parameters and engine outputs and account for the system's intrinsic non-linearities and interactions. This dramatically improves standard prediction approaches, often assuming linear correlations or disregarding variable interdependencies.

In the welding of materials, metal inert gas (MIG), gas tungsten arc welding (GTAW), and tungsten inert gas (TIG), are typical welding technologies. Welding titanium alloys involves several challenges due to their susceptibility to oxidation phenomena. Shielding arrangements of a relatively new type are tested to overcome this contamination. A proposed design and configurations are used to join commercially pure titanium sheets with variations in ARC, GMAW, and GTAW process parameters along with travel speed and welding current. Variations were made to process parameters toward full penetration butt joints in experimental bead-on plate (BoP) trials with a sheet thickness of 5 mm. Macrostructure images were subsequently captured. Analysing the microstructure of the heat-affected zone, base metal, and fusion zone is done using optical microscopy. TIG welding is 7.39

With increasing regulations about global warming, environmental pollution, and climate change, reducing carbon emissions from energy-intensive industrial activities routes to sustainable production. Because of its robust thermo-physical qualities at elevated temperatures, Monel 400 alloy is a renowned material for employment in modern aviation, medical tools, and prosthetic parts. Though, its structural stability imparts its low thermal conductivity that causes heat accumulation at the tool-workpiece contact during machining, resulting in tool cutting-edge damage. Many bio-based cutting fluids have been already tried to curtail heat generation and environmental footprints to progress overall machinability. In this endeavor, the effectiveness of dry, minimum quantity lubrication (MQL), cryogenic carbon dioxide (CO2) and Nano based MQL (N-MQL) are evaluated in terms of important sustainability indicator Carbon emission (CE). Multi-walled carbon Nano-tubes (MWCNT) in MQL oil limit the friction at the contact region which in turn reduces the power consumption. The highest CE value was found under a dry (0.0051 Kg-CO2) cutting environment and the lowest with N-MQL (0.0014 Kg-CO2). The sustainability assessment was done for CE with the help of Machine learning (ML) techniques like Decision tree (DT), Naïve Bayes, Random Forest (RF), and Support Vector Machine (SVM). Finally, when the CE levels are discretized while considering industrial needs, SVM paired with the Synthetic Minority Over-sampling approach (SMOTE) demonstrated an accuracy of almost around 100%.

The current desire is for enhanced combustion techniques that improve engine efficiency while successfully reducing emissions. Reactivity controlled compression ignition (RCCI) combustion encompasses the ability to increase fuel efficiency and decrease oxides of nitrogen emissions compared to conventional compression ignition (CI) combustion. This study presents a novel investigation that focuses on the capabilities of the RCCI engine, utilizing a unique combination of gasoline and diesel blended with cashew nut shell oil biodiesel (CNSOB) and the addition of aluminum oxide (Al2O3) nanoparticles. CNSOB extracted from waste shell has an oil content of 30-35 wt% yield. The cost of the oil range between is (sic). 40-50 in India. The engine operates on a blend of gasoline, known for its low reactivity, and diesel blended with CNSOB, a renewable and environmentally friendly alternative to diesel. The effects of varying proportions of the blend (B0, B50, and B100) and the addition of Al2O3 nano additive (25 and 50 ppm) on the efficiency and emissions of RCCI combustion are comprehensively analyzed experimentally. The results reveal that RCCI combustion significantly improves engine performance compared to compression ignition engines. A 2% increase in maximum in-cylinder pressure indicates improved combustion efficiency. Furthermore, fuel economy is improved by up to 2% due to increased engine brake thermal efficiency (BTE). Further, nitrogen oxide (NOx) emissions are reduced by up to 54% during RCCI combustion, demonstrating its potential for reducing pollution. Utilizing CNSOB biodiesel results in a 50% reduction in hydrocarbon (HC) emissions as well as a 17% reduction in carbon monoxide (CO) emissions. This indicates improved air quality and reduces environmental pollution. The incorporation of Al2O3 nano additives into the RCCI engine results in a 5% reduction in brake specific energy consumption (BSEC), showing better engine energy efficiency. Based on the results of the investigation, a neural network framework was developed. The neural network framework was developed to replicate both the efficiency and pollution parameters of the RCCI engine using the experimentally determined relationship between input factors and output parameters. The neural network model was trained using 70% of the available data, and its performance was evaluated by comparison of predictions with the corresponding observations from experiments. The forecast outcomes matched those of the trial, demonstrating the artificial neural network (ANN) models effectiveness and precision in predicting the engine performance and emissions.