Explore the vast range of titles available.

In this research, the influences of cryogenically treated stainless steel grade 317 on the eco-friendly near-dry wire-cut electrical discharge machining (NDWEDM) processes have been investigated using the minimum quantity of water mixed with oxygen gas (oxygen mist) dielectric fluid. The stainless steel grade 317 has been applied to make the various biomedical and industrial components due to its high creep strength. The wire wear ratio (WWR) and cutting rate (CR) of NDWEDM are compared using cryogenically treated and untreated work materials by Taguchi’s analysis. The water flow rate, gas pressure, spark current, and pulse width had been considered as process parameters. The microstructure of wire electrode and machined surfaces of treated/untreated materials had been compared by scanning electron microscope (SEM) images. The WWR and CR of cryogenically treated materials in NDWEDM are 20.31% lower and 22.32% higher than untreated materials, respectively.

Wire-cut electrical discharge machining (WEDM) is the highly essential unconventional electrothermal machining process to cut the contour profile in the hard materials in modern production industries. The various environmental impacting contaminants (by evaporating and reacting liquid dielectric fluid) have been produced during the conventional WEDM process and are harmful to the machine operators. These wastes have been minimized by the near-dry WEDM process in which the pressurized air mixed with a small amount of water is used as a dielectric medium. In this research, influences of machining parameters (air pressure, flow rate mixing water, spark current, and pulse width) on gas emission concentration (GEC), material removal rate (MRR), and relative emission rate (RER) of near-dry WEDM process have been optimized by the Taguchi analysis. RER has been determined to analyze the variations of gas emission concentration per unit quantity of material removal by changing the process parameters. It was revealed that the maximum air pressure and flow rate of mixing water have been predicted as significant parameters on GEC and RER. While comparing wet and near-dry WEDM processes, the material removal rate of near-dry process is comparable to that of wet WEDM with minimum GEC and RER.

In this study, a near-dry electrical discharge machining (NDEDM) process has been conducted using compressed air mixed with a low quantity of biodegradable refined sunflower oil (called oil-mist) to investigate the machining characteristics. The Box-Behnken method looks at how oil flow rate (OR), air pressure (AR), spark current (SC), and pulse width (PW) affect gas emission concentration (GEC), material removal rate (MRR), and surface roughness (SR). The TOPSIS (The Technique for Order of Preference by Similarity to the Ideal Solution) technique estimates the parameter optimal set for the best machining characteristics. The optimal machining parameters have been used to examine the microstructure of the machined surfaces using a scanning electron microscope (SEM) and energy-dispersive X-ray spectroscopy (EDS) analysis. The 0.981 mg/min of GEC, 55.145 mg/min of MRR, and 2.43 µm of surface roughness have been attained by the 14 ml/min flow rate, 7 bar of air pressure, 10 A spark current, and 48 µs pulse duration of the sun-flower oil-mist NDEDM process.

The near-dry electrical discharge machining processes have been conducted using air-mist or gas mist as a dielectric fluid to minimize the environmental impacts. In this article, near-dry electrical discharge machining (NDEDM) experiments have been performed to improve machining performance using an oxygen-mist dielectric fluid, a copper composite electrode, and Cu-Al-Be polycrystalline shape memory alloy (SMA) work materials. The copper composite electrode is made up of 12 wt% silicon carbide and 9 wt% graphite particles. The oxygen-mist pressure (Op), pulse on time (Ton), spark current (Ip), gap voltage (Gv), and flow rate of mixed water (Fr) were used as process parameters, and the material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR) were used as performance characteristics. The global optimal alternative solution has been predicted by the PROMETHEE-II (Preference Ranking Organization METhod for Enrichment Evaluations-II) optimization technique. The best combinations of process parameters have been used to examine the microstructure of composite tools and SMA-machined surfaces by scanning electron microscopy (SEM) analysis. The best global optimum settings (oP: 9 bar, Ip: 60 µs, Ip: 12 A, Gv: 40 V, and Fr: 12 ml/min) are predicted to attain optimum machining performance (MRR: 39.049 g/min, TWR: 1.586 g/min, and SR: 1.78 µm). The tool wear rate of the NDEDM process has been significantly reduced by the copper composite electrode due to increasing microhardness, wear resistance, and melting point. When compared to the pure copper electrode tool, the MRR of NDEDM is improved to 21.91%, while the TWR and SR are reduced to 46.66% and 35.02%, respectively.

In this research, the cryogenically treated superconductive Monel-K500 alloy has been machined by Wire-cut Electrical Discharge Machining (WEDM) process to improve the machining characteristics. Initially, the Monel-K500 Alloy has been cryogenically treated using − 165 °C temperature of liquid nitrogen to convert the superconductive state alloy with minimum electric resistivity. The WEDM experiments have been performed using process parameters: Spark Current (SC), Pulse Width (PW), Pulse Interval (PI), Flushing Flow rate (FF), Wire Tension (WT), and Wire Feed rate (WF), and machining characteristics: Surface roughness (Ra), Material Removal Rate (MRR), and Wire Wear Ratio (WWR) by Taguchi L27 orthogonal array. The CRITIC (CRiteria Importance Through Inter-criteria Correlation) weight integrated VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) multi-objective optimization technique has been applied to predict the combination of process parameter settings to achieve the best machining characteristics. The predicted optimum combinations of process parameter settings have been applied to compare the WEDM performances using superconductive and normal conductive states of work materials. It was revealed from comparative studies that MRR and WWR of SCS are 14.29% and 5.48% higher and the surface roughness of SCS is 26.92% lower than NCS of Monel K500 alloy, respectively.

The data aggregation process in this study has been enhanced by the hybrid firefly-artificial bee colony algorithm (HFABC) by increasing the average packet delivery ratio, end-to-end delay, and lifespan computation. In this study, HFABC and Multi Hop LEACH are two algorithms that are used to aggregate IoT data. Their performance is compared using evaluation criteria including average End-to-End Delay, PDR, and network lifetime. The HFABC method reduces average End-to-End Delay more effectively than Multi Hop LEACH, with gains of 2.20 to 8.66 %. This demonstrates how well it works to reduce the lag times for data transfer in IoT networks. With improvements ranging from 3.45% to 45.39%, HFABC has a greater success rate than Multi Hop LEACH in effectively delivering packets. HFABC increases network lifetime by 0.047 to 2.286 percent, indicating that it helps keep IoT networks operating for longer. For effective data aggregation in IoT networks, HFABC is a superior solution that decreases delays, improves packet delivery, and lengthens network lifetime.

The utilization of waste from various sources plays an important role in minimizing environmental pollution and civil construction costs. In this research, the mechanical properties of concrete were studied by mixing electronic waste (EW), glass powder (GW), and ceramic tile waste (CW). The effects of weight percentages of EW, GW, and CW are considered to investigate improvements in mechanical properties such as compressive strength (CS), split tensile strength (STS), and flexural strength (FS) of concrete. Taguchi analysis has been applied to predict the optimum composition of waste mixing percentages. The Multi-Objective Optimization Ratio Analysis (MOORA) techniques are applied to estimate the optimum composition of mixing wastes for maximizing the CS, STS, and FS of concrete. It was observed that 10 wt.% of EW, 15 wt.% of GW, and 30 wt.% of CW are predicted as the optimal mixing combinations to obtain a maximum compressive strength of 48.763 MPa, a split tensile strength of 4.178 MPa, and a flexural strength of 7.737 MPa, respectively. Finally, the predicted optimum waste-mixed weight percentages were used to examine the microstructure and various elements in the concrete using SEM and XRD analysis. When compared to conventional concrete, the optimum waste-mixed concrete has improved its compressive strength (38.453%), split tensile strength (41.149%), and flexural strength (36.215%).

The data aggregation process in this study has been enhanced by the hybrid firefly-artificial bee colony algorithm (HFABC) by increasing the average packet delivery ratio, end-to-end delay, and lifespan computation. In this study, HFABC and Multi Hop LEACH are two algorithms that are used to aggregate IoT data. Their performance is compared using evaluation criteria including average End-to-End Delay, PDR, and network lifetime. The HFABC method reduces average End-to-End Delay more effectively than Multi Hop LEACH, with gains of 2.20 to 8.66 %. This demonstrates how well it works to reduce the lag times for data transfer in IoT networks. With improvements ranging from 3.45% to 45.39%, HFABC has a greater success rate than Multi Hop LEACH in effectively delivering packets. HFABC increases network lifetime by 0.047 to 2.286 percent, indicating that it helps keep IoT networks operating for longer. For effective data aggregation in IoT networks, HFABC is a superior solution that decreases delays, improves packet delivery, and lengthens network lifetime.

Polymer nanocomposites have been synthesized by the covalent addition of bromide-functionalized graphene (Graphene-Br) through the single electron transfer-living radical polymerization technique (SET-LRP). Graphite functionalized with bromide for the first time via an efficient route using mild reagents has been designed to develop a graphene based radical initiator. The efficiency of sacrificial initiator (ethyl α-bromoisobutyrate) has also been compared with a graphene based initiator towards monitoring their Cu(0) mediated controlled molecular weight and morphological structures through mass spectroscopy (MOLDI-TOF) and field emission scanning electron microscopy (FE-SEM) analysis, respectively. The enhancement in thermal stability is observed for graphene-grafted-poly(methyl methacrylate) (G-g-PMMA) at 392 °C, which may be due to the influence ofthe covalent addition of graphene, whereas the sacrificial initiator used to synthesize G-graft-PMMA (S) has low thermal stability as analyzed by TGA. A significant difference is noticed on their glass transition and melting temperatures by DSC. The controlled formation and structural features of the polymer-functionalized-graphene is characterized by Raman, FT-IR, UV-Vis spectroscopy, NMR, and zeta potential measurements. The wettability measurements of the novel G-graft-PMMA on leather surface were found to be better in hydrophobic nature with a water contact angle of 109 ± 1°.