Experimental investigation and empirical modeling of the dry electric discharge machining process

Dry electric discharge machining (EDM) is an environment-friendly modification of the oil EDM process in which liquid dielectric is replaced by a gaseous medium. In the current work, parametric analysis of the process has been performed with tubular copper tool electrode and mild steel workpiece. Experiments have been conducted using air as the dielectric medium to study the effect of gap voltage, discharge current, pulse-on time, duty factor, air pressure and spindle speed on material removal rate (MRR), surface roughness (R_a) and tool wear rate (TWR). First, a set of exploratory experiments has been performed to identify the optimum tool design and to select input parameters and their levels for later stage experiments. Empirical models for MRR, R_a and TWR have then been developed by performing a designed experiment based on the central composite design of experiments. Response surface analysis has been done using the developed models. Analysis of variance (ANOVA) tests were performed to identify the significant parameters. Current, duty factor, air pressure and spindle speed were found to have significant effects on MRR and R_a. However, TWR was found to be very small and independent of the input parameters.     

Review of tools and algorithms for network motif discovery in biological networks

Network motifs are recurrent and over‐represented patterns having biological relevance. This is one of the important local properties of biological networks. Network motif discovery finds important applications in many areas such as functional analysis of biological components, the validity of network composition, classification of networks, disease discovery, identification of unique subunits etc. The discovery of network motifs is a computationally challenging task due to the large size of real networks, and the exponential increase of search space with respect to network size and motif size. This problem also includes the subgraph isomorphism check, which is Nondeterministic Polynomial (NP)‐complete. Several tools and algorithms have been designed in the last few years to address this problem with encouraging results. These tools and algorithms can be classified into various categories based on exact census, mapping, pattern growth, and so on. In this study, critical aspects of network motif discovery, design principles of background algorithms, and their functionality have been reviewed with their strengths and limitations. The performances of state‐of‐art algorithms are discussed in terms of runtime efficiency, scalability, and space requirement. The future scope, research direction, and challenges of the existing algorithms are presented at the end of the study.Inspec keywords: computational complexity, graph theory, biology, search problemsOther keywords: network size, motif size, network motif discovery, biological networks, network composition, recurrent patterns, over‐represented patterns, local properties, search space, subgraph isomorphism check, NP‐complete problem, NP‐complete problem, exact census, design principles, background algorithms, runtime efficiency, space requirement     

Structural and electrical properties of ultra-thin high-k ZrO2 film on nitride passivated Ge(100) prepared by PEALD

The study of ultrathin ZrO₂ films grown on surface passivated germanium substrates by plasma enhanced Atomic Layer Deposition (PEALD) has been carried out. Nitride passivation has been used to form an interfacial layer between ZrO₂/Ge. The ultra-thin ZrO₂ film deposited with thickness of ~5.75 nm and refractive index of ~2.05 as observed through ellipsometry. The Ge3d, Zr3d, N1s and O1s are XPS core level spectra's confirm the formation of GeON and ZrO₂ ultra-thin films. The AFM results show the roughness of deposited films as low as 0.3 nm. The effect of post metallization annealing (PMA) on electrical properties of Au/Cr/ZrO₂/GeON/Ge capacitors has been investigated. The improvement in k value (~38) and an EOT value (~0.5 nm) after PMA on Ge/GeON/ZrO₂ stack has been observed. The flat band voltage and hysteresis of post metallization annealed devices has been reduced as compared to that of without PMA GeON/ZrO₂ stack.     

Biological treatment of precious metal refinery wastewater: A review

The refining of platinum group metals (PGMs) generates large volumes of wastewater which is highly contaminated by organic solvents and contains trace amounts of heavy metals. Treatment to reduce chemical oxygen demand and metal concentration to levels allowing reuse in refinery processes can help to alleviate the demand for clean water in arid/semi arid mining regions of the world. Traditional physicochemical treatment options have been favoured in the past for treatment of PGM wastewater but biological treatment is becoming increasingly popular. This review examines the need for treatment of PGM wastewater and various physicochemical technologies that are available for treatment of organic solvents and heavy metals. It also introduces various activated sludge technologies that have been shown to remove 99% of certain solvents, while biosorption has been demonstrated to be very effective in removal of heavy metals. A combination of biological treatment and biosorption can be a viable technology for the treatment of complex and potentially toxic wastewaters. Improved treated wastewater quality can allow for reuse in refinery processes which could lead to significant cost reduction and prove to be environmentally beneficial.     

A fuzzy filter for improving the quality of the signal in adaptive-network-based fuzzy inference systems (ANFIS)

This study is addressed to improve the quality of the signal of the Adaptive-Network based Fuzzy Inference System (ANFIS) reducing the level of fluctuations in the output due to periodical disturbances. The fuzzy filter computes the disturbances as periodic signals with two components, one at high frequency and another at low frequency. It was incorporated in the layer 0 of the network reducing iteratively effectively the heavy noise.     

Numerical analysis of laminar flow heat transfer of nanofluids in a flattened tube

In this work, a three-dimensional analysis is used to study the heat transfer performance of nanofluid flows through a flattened tube in a laminar flow regime and constant heat flux boundary condition. CuO nanoparticles dispersed in ethylene glycol with particle volume concentrations ranging between 0 and 4 vol.% were used as working fluids for simulating the heat transfer of nanofluids. Effects of some important parameters such as nanoparticle volume concentration, particles Brownian motions, and Reynolds number on heat transfer coefficient have been determined and discussed in details. Results have shown that the heat transfer coefficient increases with increase in the volume concentration level of the nanoparticle, Brownian motion and the Reynolds number. Numerical results have been validated by comparison of simulations with those available in the literature.     

Monte Carlo analysis of wind farm surge arresters risk of failure due to lightning surges

This paper presents a Monte Carlo procedure intended for the assessment of the metal-oxide (MO) surge arresters risk of failure in onshore wind farms. It focuses on the energy withstand (absorption) capability of the MO surge arresters in relation to lightning surges and in terms of their risk of failure assessment. Presented methodology accounts for the fact that the lightning itself is stochastic in nature and that the MO surge arrester energy capability is a statistical quantity. The well-known backsurge phenomenon is employed as a means for studying the MO surge arresters energy stresses due to lightning surge transients (in onshore wind farms), where the associated transient (i.e. high-frequency) models of particular wind farm components feature prominently. Necessary numerical simulations are carried-out with the well-known EMTP-ATP software package. This procedure could be seen as beneficial in selection of the optimal MO surge arrester energy withstand capability for wind farm projects situated in areas marked with high keraunic levels and/or having high soil resistivity.     

Demand forecasting for production planning in remanufacturing

The International Journal of Advanced Manufacturing Technology - Remanufacturing is effective for energy and material savings; however, production planning and control in remanufacturing are more...     

Assessment of the effects of rice husk ash particle size on strength,water permeability and workability of binary blended concrete

This study develops the compressive strength, water permeability and workability of concrete by partial replacement of cement with agro-waste rice husk ash. Two types of rice husk ash with average particle size of 5 micron (ultra fine particles) and 95 micron and with four different contents of 5%, 10%, 15% and 20% by weight were used. Replacement of cement up to maximum of 15% and 20% respectively by 95 and 5 μm rice husk ash, produces concrete with improved strength. However, the ultimate strength of concrete was gained at 10% of cement replacement by ultra fine rice husk ash particles. Also the percentage, velocity and coefficient of water absorption significantly decreased with 10% cement replacement by ultra fine rice husk ash. Moreover, the workability of fresh concrete was remarkably improved by increasing the content of rice husk ash especially in the case of coarser size. It is concluded that partial replacement of cement with rice husk ash improves the compressive strength and workability of concrete and decreases its water permeability. In addition, decreasing rice husk ash average particle size provides a positive effect on the compressive strength and water permeability of hardened concrete but indicates adverse effect on the workability of fresh concrete.     

Determining the flow correlation for an orifice with a non-dimensional number

Traditional devices like orifice meters play a crucial function as a flow measuring device because there is inaccuracy in the measurement of the flow measuring device concern. The pressure drop (Δp) between the upside and downsides of the orifice-pipe flow passage is calculated using Bernoulli's principle. Orifice meter produces errors and uncertainty in the downstream of the flow because of wake or backflow. The proposed study provides the procedure to calculate the Δp and flow characteristics for a circular orifice for a compressible fluid (Air) with CFD analysis. The numerical study was carried out by considering combined parameters such as area ratio (σ) and space ratio (s) as geometrical parameters and Reynolds number as flow parameters to minimize the errors of the numerical calculation. The input parameter σ varies from 0.2 to 0.6, and the s varies from 0.1 to 0.9. Whereas the Reynolds number (Re) varies from 10000 to 100000. A non-dimensional number is defined by the combined effect of σ and s to generated correlations with accuracy which is enhanced predicted results of the work. The correlation will make a significant contribution to the flow monitoring device design.