Search

The current research aims at analyzing the machining performance measures of titanium diamond using electro-discharge machining (EDM) process. The selected control variables are peak current and pulse-on-time (Ton) while maintaining a 50% to 75% duty factor during experimental runs. The output responses considered for this operation are material removal rate (MRR), tool wear rate (TWR) and average surface roughness (Ra). A full factorial design (L16) has been employed to execute the current investigation. Analysis of variance along with overall evaluation criteria (OEC) is also carried out for determining the optimal input parametric combination. The work is further supported by mean effect plots and 3D performance plots for visual illustration of outputs. Analyzed results show that current has the most impact on MRR and Ra, while Ton has the most impact on TWR. The obtained results show MRR of 7.753 mg/min (highest), TWR of 0.02 mg/min (lowest) and Ra of 5.46 µm at the optimum parametric combination. Moreover, x-ray diffraction (XRD), scanning electron micrograph (SEM), and energy-dispersive x-ray (EDX) analysis also help in identifying different phases of material on machined work surface. [ABSTRACT FROM AUTHOR]

Present study focuses on the production and characterization of stir cast Al7075-TiB2-B4C hybrid composite. Different weight % of second phase reinforcement has been added with 2, 4, 6, 8 % TiB2, and 4 % B4C. The morphology of the composites shows uniform distribution of reinforcement particles and improved microstructure. As the proportion of TiB2-B4C is increasing in the composite, the density, hardness, and strength have been enhanced. The Al7075-TiB2-B4C composite shows a higher hardness of 93 HRB and improved wear resistance. Moreover, the enhanced yield strength of 522 MPa and compressive strength of 379 MPa have been obtained. A dry sliding wear test was conducted at room temperature by pin-on-disc apparatus under 5 N and 10 N loads, with 400 m, 800 m, and 1200 m sliding distances. The wear resistance of TiB2-B4C composites is highly improved and worn surface damage is reduced. The hardness, tensile strength, and compressive strength of the Al7075-TiB2-B4C composite were enhanced by 17.5%, 15.2%, and 10.8% while compared to raw Al. Worn surface analysis, mechanism of wear, and microstructural changes were described by SEM analysis. On the outset, the incredible properties of Al7075-TiB2-B4C hybrid composites are the best replacement for upper wing applications. [ABSTRACT FROM AUTHOR]

Shallow foundations are uses for safe transmission of structural load to the ground considering soil bearing capacity and allowable settlement. In this research, an empirical analysis is present by using Terzaghi and Meyerhof model. Which is compared with numerical analysis using PLAXIS 3D Foundation. Bearing capacity determined by both cases matched well. The purpose of the study is to predict the maximum allowable stress capacity of shallow foundation in different conditions and establish relationships between stress and settlements for different footing dimensions under various circumstances, as well as the impact of pressure bulb overlapping of two adjacent footing. There are some notable findings are found in this study such as Ultimate stress bearing capacity of soil under allowable settlement varies if two adjacent footings are placed 1.5 m apart. Similar results have been found by varying spacing 2 m & 2.5 m. Sizes of footing may have insignificant effect on spacing between two adjacent footings. And footing may act as an isolated footing in 3 m away from another footing on sandy soil and 2 m away from another footing on clayey soil. [ABSTRACT FROM AUTHOR]

The edge/fog computing has the potential to gear up the healthcare industry by providing better and faster health services to the patients. In healthcare systems where every second is crucial, the edge computing can be helpful to reduce the time between data capture and analytics in a powerful manner. In edge computing, the network edge devices are configured in such a manner that they can handle critical analysis and make necessary decisions instead of sending the captured health data directly to the cloud. However, lifetime of the edge network is a critical factor and thus an energy efficient network architecture has to be designed to achieve the above mentioned goal. In this regard, this research presents a new extremal optimization tuned micro genetic algorithm (EO-μGA) based clustering technique for the sake of efficient routing and prolonging network lifetime by saving the battery power of network edge devices. Moreover, a novel fitness function with a set of relevant criteria of edge devices such as energy factor, average intra-cluster distance, average distance to cluster leader over data analytics center, average sleeping time, and computational load has been considered for the selection of the cluster leader which will be responsible for managing intra-cluster and inter-cluster data communication. The simulation results show that the proposed EO-μGA based clustering model offers a higher network lifetime and a least amount of transmission energy consumption per node as compared to various state of the art optimization algorithms. [ABSTRACT FROM AUTHOR]

Bi2Mn4O10 was synthesized from corresponding metal salts in glycerin by using an organic precursor-based glycerin nitrate method. The precursor was heated at various temperatures (300 - 800 °C) for about 18 hours to determine the lowest synthesis temperature for the formation of Bi2Mn4O10. The XRD patterns of the calcined samples revealed that the desired mullite type phase started to form at 600 °C, which became more crystalline with further increase of calcination temperature. Attempts were also taken to prepare chromium and cobalt incorporated solid solution series with nominal composition Bi2MxMn4-xO10 (M = Cr3+ and Co3+) by the same procedure. The XRD data of these series exhibited mullite type single phase up to x = 0.7 and 0.1 compositions for chromium and cobalt, respectively. For further insertion of M, an extra phase appeared along with the mullite type phase. [ABSTRACT FROM AUTHOR]

In the present work, machining of boron carbide (B4C) has been performed using ultrasonic machining (USM) with the same material as abrasive particles. A comparative assessment of machining of B4C with that of glass and brass has also been carried out by drilling microholes using USM. The experimentation has been conducted according to Taguchi L9 orthogonal array. The effect of feed rate, slurry concentration, and grit size has been evaluated on the performance measures such as material removal rate, overcut, circularity error, taper angle and tool wear rate. Additionally, overall evaluation criteria (OEC) has been formulated to evaluate the combined performance measures. After ANOVA feed rate became the most significant parameters affecting OEC for all the materials. The machining of B4C has been found to be possible but lacks in the machining rate in comparison with that of glass and brass. Least tool wear rate has been found in case of glass whereas minimum overcut, circularity error and taper angle have been observed for brass. The quality of the drilled holes in B4C is better as minimal surface chipping and burr formation has been observed. [ABSTRACT FROM AUTHOR]

The fabrication of micro-fin is very critical due to its higher debility nature. In this paper, a simple methodology is proposed for fabricating an array of micro-fins using 2-Axis wire electric discharge machining process, which gives a faster production rate with good accuracy. Arrays of 625 (25 × 25) micro-fins have been fabricated on stainless steel and copper square rods. The average height of the fabricated fins is 850 µm. The effects of process parameters such as pulse-on-time, pulse-off-time, current, and table feed on performance measures like material removal rate, overcut, and cutting rate have been studied. Taguchi L16 orthogonal array has been employed to conduct the experimentation. Analysis of variance (ANOVA) has been applied to observe the significant process parameters and their contribution to the responses. Moreover, the overall evaluation criteria (OEC) have also been formulated to obtain the best parametric combination. The characterization of fabricated micro-pin-fins has been carried out using scanning electron microscope and energy dispersive X-ray techniques. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to design an efficient and robust method to predict the power production from the anaerobic digester by taking into consideration various factors and feedstocks (organic fraction municipal solid waste, wastewater sludge, and co-digestion). This study focuses on the influence of primary factors such as temperature, pH, and nitrogen concentration on power production. Extremal neural network tool was developed with the assistance of MATLAB programming to predict the power production. The results showed that this approach is trustworthy enough to predict the energy output with respect to the primary variables mentioned above. It is expected that this new prediction model will improve the biogas productivity and contribute significantly to energy production. Moreover, this design has an economic advantage in processing. [ABSTRACT FROM AUTHOR]

Samples of nominal compositions, CsNbWO and CsNbWO with 0.0 ≤ y ≤ 0.25 and 0.0 ≤ y ≤ 0.3 were synthesized using appropriate amounts of CsWO, WO and WO in evacuated and closed silica glass tubes at 800 °C. The polycrystalline products contain hexagonal shaped crystals of up to 15 μm diameter as long as y ≤ 0.15. X-ray powder patterns of the samples reveal the formation of hexagonal tungsten bronze (HTB-I) type phase with y < 0.1. A mixture of HTB-I and an analogous less reduced hexagonal tungsten bronze (HTB-II) type phase is seen when y ≥ 0.1. HTB-II content increases with increasing y, revealing close similarity to bronzoid type phases when y = x. Results of SEM/EDX analysis also support a partial substitution of tungsten by niobium in the HTB-I type phase. Infrared absorption and optical reflectivity data shows the effect of increasing amount of non-metallic phase for y > 0.1 and the effect of counterdoping by Nb/W substitution in the metallic HTB-I type phase for y ≤ 0.1, respectively. Reinvestigations in the system RbNbWO (0.0 ≤ y ≤ 0.175) show similar results with increasing content of HTB-II type phase related with y. [ABSTRACT FROM AUTHOR]

Needle-shaped crystals of sizes up to 5 μm × 5 μm × 40 μm of nominal composition K x Nb y W1− y O3 were synthesized by solid state method at 800 °C using appropriate amount of WO3, WO2, Nb2O5, and K2WO4. The samples were characterized with XRD, SEM, microprobe analysis, optical spectroscopy, and Raman spectroscopy. The XRD patterns of the samples show single phase of tetragonal tungsten bronze (TTB) type (P4/mbm, No. 127) up to y = 0.07. Structure refinements reveal an increase in cell parameter with increasing nominal niobium content within the TTB phase. The elemental compositions of the crystals determined by electron microprobe analysis also show an increase in Nb content with increasing y. With increasing Nb content the reflectivity minimum in the near infrared spectral range shifts towards lower wavenumber indicating the effect of decreasing carrier concentration. Pyrochlore type phase (KNbWO6) is obtained as a second phase when nominal composition y > 0.07. [ABSTRACT FROM AUTHOR]

Kyasanur Forest Disease (KFD) is a life-threatening tick-borne viral infectious disease endemic to South Asia and has been taking so many lives every year in the past decade. But recently, this disease has been witnessed in other regions to a large extent and can become an epidemic very soon. In this paper, a new fog computing based e-Healthcare framework has been proposed to monitor the KFD infected patients in an early phase of infection and control the disease outbreak. For ensuring high prediction rate, a novel Extremal Optimization tuned Neural Network (EO-NN) classification algorithm has been developed using hybridization of the extremal optimization with the feed-forward neural network. Additionally, a location based alert system has also been suggested to provide the global positioning system (GPS)-based location information of each KFD infected user and the risk-prone zones as early as possible to prevent the outbreak. Furthermore, a comparative study of proposed EO-NN with state of art classification algorithms has been carried out and it can be concluded that EO-NN outperforms others with an average accuracy of 91.56%, a sensitivity of 91.53% and a specificity of 97.13% respectively in classification and accurate identification of risk-prone areas. [ABSTRACT FROM AUTHOR]