Optimization of Machining Parameters for Milling Operations Using Non-conventional Methods

In this paper, optimization procedures based on the genetic algorithm, tabu search, ant colony algorithm and particle swarm optimization Algorithm were developed for the optimization of machining parameters for milling operation. This paper describes development and utilization of an optimization system, which determines optimum machining parameters for milling operations. An objective function based on maximum profit in milling operation has been used. An example has been presented at the end of the paper to give a clear picture from the application of the system and its efficiency. The results are compared and analysed using the method of feasible directions and handbook recommendations.     

Particle swarm optimization (PSO) algorithm for optimal machining allocation of clutch assembly

Nowadays tolerance optimization is increasingly becoming an important tool for manufacturing and mechanical design. This seemingly, arbitrary task of assigning dimension tolerance can have a large effect on the cost and performance of manufactured products. With the increase in competition in today’s market place, small savings in cost or small increase in performance may determine the success of a product. In practical applications, tolerances are most often assigned as informal compromises between functional quality and manufacturing cost. Frequently the compromise is obtained interactively by trial and error. A more scientific approach is often desirable for better performance. In this paper particle swarm optimization (PSO) is used for the optimal machining tolerance allocation of over running clutch assembly to obtain the global optimal solution. The objective is to obtain optimum tolerances of the individual components for the minimum cost of manufacturing. The result obtained by PSO is compared with the geometric programming (GP) and genetic algorithm (GA) and the performance of the result are analyzed .     

Low-loss 7-bit S-band CMOS passive phase shifter with digital control

This paper presents the design and implementation of a 7-bit S-band digital passive phase shifter using Complementary Metal-Oxide-Semiconductor (CMOS) 65-nm technology in 2.6- to 3.2-GHz frequency band. New switched delay network topology has been used for 5.625° and 2.8°, and modified switched filter topology has been used for implementation of other phase bits to achieve 7-bit performance with low insertion loss and better isolation. The measured results of the fabricated chip show 7-bit performance with an average insertion loss of 11 dB, average root mean square (RMS) phase error of less than 2.0°, average RMS amplitude error of less than 0.6 dB, input matching (S₁₁) better than −7.5 dB, and output matching (S₂₂) better than −14.5 dB across the target frequency band at 50Ω input/output impedance.     

Application of particle swarm optimisation in artificial neural network for the prediction of tool life

In an advanced manufacturing system, accurate assessment of tool life estimation is very essential for optimising the cutting performance in turning operation. Estimation of tool life generally requires considerable time and material and hence it is a relatively expensive procedure. In this present work, back-propagation feed forward artificial neural network (ANN) has been used for tool life prediction. Speed, feed, depth of cut and flank wear were taken as input parameters and tool life as an output parameter. Twenty-five patterns were used for training the network. Recently there have been significant research efforts to apply evolutionary computational techniques for determining the network weights. Hence an evolutionary technique named particle swarm optimisation has been used instead of the back-propagation algorithm and it is proved that the experimental results matched well with the values predicted by both artificial neural network with back-propagation and the proposed method. It is found that the computational time is greatly reduced by this method.     

Surface Modification of Commercial Low-Carbon Steel using Glow Discharge Nitrogen Plasma and its Characterization

Plasma nitriding under glow discharge nitrogen plasma has been undertaken on laboratory scale for surface engineering of commercial low carbon steels. The treatment has been shown to confer exceptional improvement in surface properties, viz., hardness and corrosion resistance. The results have been discussed in light of microstructural changes occurring on steel surface and its interior as a result of Fickian nitrogen diffusion and correlated with influences of nitriding-temperature and alloying elements (Mn, Nb, and Si) in steel.     

Tuning violet to green emission in luminomagnetic Dy,Er co-doped ZnO nanoparticles

This paper discusses the synthesis of undoped ZnO, 2?mol% Dy doped ZnO, 2?mol% Er doped ZnO and 1?mol% Dy,Er co-doped ZnO nanoparticles by simple combustion technique and the characterization of their structural, morphological, magnetic and optical properties by X-ray diffraction (XRD), X-Ray Photoelectron Spectroscopy(XPS), Field Emission Scanning Electron Microscope (FESEM), High Resolution Transmission Electron Microscope (HRTEM), Diffuse Reflectance Spectroscope (DRS), Vibrating Sample Magnetometer (VSM) and Photoluminescence(PL). All samples are of hexagonal wurzite type structure which was found from XRD analysis. The effects of annealing on morphology and luminescence emission wavelengths were noticed in FESEM and PL, respectively. As-prepared sample displayed spherical morphology and annealed co-doped sample showed interwoven hexagonal stacking like morphology. VSM revealed the room temperature ferromagnetism in doped samples. The photoluminescence under the UV and IR excitations was observed in experiment. The as-prepared samples had violet region emission at the 325?nm excitation. The annealed samples had green region emission under the same excitation. Due to the annealing effect, the enhancement of upconversion luminescence intensity in co-doped sample in green (535?nm) and red (665?nm) regions was observed at the 980?nm excitation.     

Experimental investigation on a DI diesel engine fuelled with Madhuca Indica ester and diesel blend

Biodiesel is a fatty acid alkyl ester, which is renewable, biodegradable and non-toxic fuel which can be derived from any vegetable oil by transesterification. One of the popularly used biodiesel in India is Mahua oil (Madhuca Indica). In the present investigation Mahua oil was transesterified using methanol in the presence of alkali catalyst and was used to study the performance and emission characteristics. The biodiesel was tested on a single cylinder, four stroke compression ignition engine. Engine performance tests showed that power loss was around 13% combined with 20% increase in fuel consumption with Mahua oil methyl ester at full load. Emissions such as carbon monoxide, hydrocarbon were lesser for Mahua ester compared to diesel by 26% and 20% respectively. Oxides of nitrogen were lesser by 4% for the ester compared to diesel.     

Emission reduction on ethanol–gasoline blend using cerium oxide nanoparticles as fuel additive

In this study, the effect of ethanol–gasoline blend with cerium oxide nanoparticles as additive on a Tata Nano twin–cylinder SI engine was investigated. In this work, the combustion, performance and emission tests were conducted. The experiment fuels were prepared using 99.9% pure ethanol and gasoline with cerium oxide nanoparticles. The volumetric percentages of ethanol–gasoline blends with cerium oxide nanoparticles additive are in the ratio of E30, E40 and E50. These represent the ratios of ethanol amount in the total blend and the rest of gasoline. Additionally, 100?mg, 150?mg and 200?mg cerium oxide nanoparticles additive are mixed to E30, E40 and E50, respectively. The venture of this investigation was to reformulate the fuel to utilize the cerium oxide nanoparticles with ethanol and gasoline blend to develop the fuel’s performance and to decrease the pollution from the engine. The experimental results expose an increase in brake thermal efficiency for the nanoparticles blends. In the emission test CO, CO₂, HC and NOx are noticeably reduced, and O₂ increased for all the blends. In combustion analyses, the cylinder pressure is higher for nanoparticles blends, when compared to that of the sole fuel.     

Synthesis of cracked Mahua oil using coal ash catalyst for diesel engine application

ABSTRACT

In this work, production of hydrocarbon fuel from Mahua oil has been characterised for diesel engine application, by appraising essential fuel processing parameters. As opposed to traditional trans-esterification process, the reported oil was identified by using heterogeneous catalysts, as the latter improves the fuel properties better than the former. Therefore, interest has been taken in utilising heterogeneous catalyst such as Coal fly ash (CFA) for biofuel production process. From the experimental investigation it was observed that performance results such as BTE for B25 blend show marginally lower value to sole fuel at all loads and NO_x emission for B100 blend exhibit significantly lower value than sole fuel. Smoke emission for B25 show the increasing trend that of other blends. However, CO, HC emission for B25 shows the marginal increases when compared to the sole fuel and the combustion analysis of B25 blend showed almost similar trend of sole fuel.     

Analytic Hierarchy Processes for Spectrum Sharing in 5G New Radio Standard

Wireless Personal Communications - The 5th Generation (5G) systems have the objective of delivering fiber like performance in a wireless environment. It is expected to connect all devices including...