Abstract: In electrical discharge machining (EDM), a complete and clear theory has not yet been established. The developed theory (physical models) yields results far from reality due to the complexity of the physics. It is difficult to select proper parameter settings in order to achieve better EDM performance. However, modelling can solve this critical problem concerning the parameter settings. Therefore, the purpose of the present work is to develop mathematical model to predict performance characteristics of EDM on Ti-5Al-2.5Sn titanium alloy. Response surface method (RSM) and artificial neural network (ANN) are employed to develop the mathematical models. The developed models are verified through analysis of variance (ANOVA). The ANN models are trained, tested, and validated utilizing a set of data. It is found that the developed ANN and mathematical model can predict performance of EDM effectively. Thus, the model has found a precise tool that turns EDM process cost-effective and more efficient.
Abstract: Modeling of hydrogen fueled engine (H2ICE) injection system is a very important tool that can be used for explaining or predicting the effect of advanced injection strategies on combustion and emissions. In this paper, a common rail injection system (CRIS) is proposed for 4-strokes 4-cylinders hydrogen fueled engine with port injection feeding system (PIH2ICE). For this system, a numerical one-dimensional gas dynamic model is developed considering single injection event for each injector per a cycle. One-dimensional flow equations in conservation form are used to simulate wave propagation phenomenon throughout the CR (accumulator). Using this model, the effect of common rail on the injection system characteristics is clarified. These characteristics include: rail pressure, sound velocity, rail mass flow rate, injected mass flow rate and pressure drop across injectors. The interaction effects of operational conditions (engine speed and rail pressure) and geometrical features (injector hole diameter) are illustrated; and the required compromised solutions are highlighted. The CRIS is shown to be a promising enhancement for PIH2ICE.
Abstract: Tribological properties that include nanoparticles are an alternative to improve the tribological behaviour of lubricating oil, which has been investigated by many researchers for the past few decades. Various nanostructures can be used as additives for tribological improvement. However, this also depends on the characteristics of the nanoparticles. In this study, tribological investigation was performed to examine the effect of CuO nanoparticles on the tribological behaviour of Syntium 800 SL 10W−30. Three parameters used in the analysis using the wear tester (piston ring) were load, revolutions per minute (rpm), and concentration. The specifications of the nanoparticles, such as size, concentration, hardness, and shape, can affect the tribological behaviour of the lubricant. The friction and wear experiment was conducted using a tribo-tester and the Response Surface Methodology method was used to analyse any improvement of the performance. Therefore, two concentrations of 40 nm nanoparticles were used to conduct the experiments, namely, 0.005 wt % and 0.01 wt % and compared with base oil 0 wt % (control). A water bath sonicator was used to disperse the nanoparticles in base oil, while a tribo-tester was used to measure the coefficient of friction and wear rate. In addition, the thermal properties of the nanolubricant were also measured. The results have shown that the thermal conductivity of the nanolubricant was increased when compared with the base oil. Therefore, the results indicated that CuO nanoparticles had improved the tribological behaviour as well as the thermal properties of the nanolubricant oil.
Abstract: An experiment was conducted to elucidate the effects of turmeric powder supplementation on egg production performance of old laying hens (80 weeks of age). There were 40 hens of Hysex Brown strain used in the study. They were caged individually, and randomly divided into 4 treatment groups of diet containing 0 (control), 1, 2 and 4 % oven dried turmeric powder for 3 periods of 4 weeks; Egg production (% hen day) and feed intake of the 4 treatment groups at the commencement of the experiment were not significantly different. In addition to egg production performance (% and egg weight), feed and water intakes were measured daily, and cholesterol content of the whole egg was determined. The results indicated that feed intakes of the hen were significantly lowered when 4% turmeric powder supplemented, while there were no significant changes in water intakes. Egg production were significantly increased and maintained at a higher level by turmeric powder supplementation up to 4% compared with the control, while the weight of eggs were not significantly affected. The research markedly demonstrated that supplementation of turmeric powder up to 4% could improve and maintain egg production performance of the old laying hen at a higher level with a lower cholesterol content.
Abstract: Organizational tendencies towards computer-based
information processing have been observed noticeably in the
third-world countries. Many enterprises are taking major initiatives
towards computerized working environment because of massive
benefits of computer-based information processing. However,
designing and developing information resource management software
for small and mid-size enterprises under budget costs and strict
deadline is always challenging for software engineers. Therefore, we
introduced an approach to design mid-size enterprise software by
using the Waterfall model, which is one of the SDLC (Software
Development Life Cycles), in a cost effective way. To fulfill research
objectives, in this study, we developed mid-sized enterprise software
named “BSK Management System” that assists enterprise software
clients with information resource management and perform complex
organizational tasks. Waterfall model phases have been applied to
ensure that all functions, user requirements, strategic goals, and
objectives are met. In addition, Rich Picture, Structured English, and
Data Dictionary have been implemented and investigated properly in
engineering manner. Furthermore, an assessment survey with 20
participants has been conducted to investigate the usability and
performance of the proposed software. The survey results indicated
that our system featured simple interfaces, easy operation and
maintenance, quick processing, and reliable and accurate transactions.
Abstract: Artificial neural networks (ANN) are used in distinct researching fields and professions, and are prepared by cooperation of scientists in different fields such as computer engineering, electronic, structure, biology and so many different branches of science. Many models are built correlating the parameters and the outputs in electrical discharge machining (EDM) concern for different types of materials. Up till now model for Ti-5Al-2.5Sn alloy in the case of electrical discharge machining performance characteristics has not been developed. Therefore, in the present work, it is attempted to generate a model of material removal rate (MRR) for Ti-5Al-2.5Sn material by means of Artificial Neural Network. The experimentation is performed according to the design of experiment (DOE) of response surface methodology (RSM). To generate the DOE four parameters such as peak current, pulse on time, pulse off time and servo voltage and one output as MRR are considered. Ti-5Al-2.5Sn alloy is machined with positive polarity of copper electrode. Finally the developed model is tested with confirmation test. The confirmation test yields an error as within the agreeable limit. To investigate the effect of the parameters on performance sensitivity analysis is also carried out which reveals that the peak current having more effect on EDM performance.
Abstract: Electrical discharge machining (EDM) is a relatively modern machining process having distinct advantages over other machining processes and can machine Ti-alloys effectively. The present study emphasizes the features of the development of regression equation based on response surface methodology (RSM) for correlating the interactive and higher-order influences of machining parameters on surface finish of Titanium alloy Ti-6Al-4V. The process parameters selected in this study are discharge current, pulse on time, pulse off time and servo voltage. Machining has been accomplished using negative polarity of Graphite electrode. Analysis of variance is employed to ascertain the adequacy of the developed regression model. Experiments based on central composite of response surface method are carried out. Scanning electron microscopy (SEM) analysis was performed to investigate the surface topography of the EDMed job. The results evidence that the proposed regression equation can predict the surface roughness effectively. The lower ampere and short pulse on time yield better surface finish.
Abstract: Thermal conductivity is an important characteristic of
a nanofluid in laminar flow heat transfer. This paper presents an
improved model for the prediction of the effective thermal
conductivity of nanofluids based on dimensionless groups. The
model expresses the thermal conductivity of a nanofluid as a function
of the thermal conductivity of the solid and liquid, their volume
fractions and particle size. The proposed model includes a parameter
which accounts for the interfacial shell, brownian motion, and
aggregation of particle. The validation of the model is verified by
applying the results obtained by the experiments of Tio2-water and
Al2o3-water nanofluids.
Abstract: Regenerative gas turbine engine cycle is presented that yields higher cycle efficiencies than simple cycle operating under the same conditions. The power output, efficiency and specific fuel consumption are simulated with respect to operating conditions. The analytical formulae about the relation to determine the thermal efficiency are derived taking into account the effected operation conditions (ambient temperature, compression ratio, regenerator effectiveness, compressor efficiency, turbine efficiency and turbine inlet temperature). Model calculations for a wide range of parameters are presented, as are comparisons with simple gas turbine cycle. The power output and thermal efficiency are found to be increasing with the regenerative effectiveness, and the compressor and turbine efficiencies. The efficiency increased with increase the compression ratio to 5, then efficiency decreased with increased compression ratio, but in simple cycle the thermal efficiency always increase with increased in compression ratio. The increased in ambient temperature caused decreased thermal efficiency, but the increased in turbine inlet temperature increase thermal efficiency.
Abstract: Cutting fluids, usually in the form of a liquid, are
applied to the chip formation zone in order to improve the cutting
conditions. Cutting fluid can be expensive and represents a biological
and environmental hazard that requires proper recycling and
disposal, thus adding to the cost of the machining operation. For
these reasons dry cutting or dry machining has become an
increasingly important approach; in dry machining no coolant or
lubricant is used. This paper discussed the effect of the dry cutting on
cutting force and tool life when machining aerospace materials
(Haynes 242) with using two different coated carbide cutting tools
(TiAlN and TiN/MT-TiCN/TiN). Response surface method (RSM)
was used to minimize the number of experiments. ParTiAlN Swarm
Optimisation (PSO) models were developed to optimize the
machining parameters (cutting speed, federate and axial depth) and
obtain the optimum cutting force and tool life. It observed that
carbide cutting tool coated with TiAlN performed better in dry
cutting compared with TiN/MT-TiCN/TiN. On other hand, TiAlN
performed more superior with using of 100 % water soluble coolant.
Due to the high temperature produced by aerospace materials, the
cutting tool still required lubricant to sustain the heat transfer from
the workpiece.
Abstract: This paper deals optimized model to investigate the
effects of peak current, pulse on time and pulse off time in EDM performance on material removal rate of titanium alloy utilizing copper tungsten as electrode and positive polarity of the electrode. The experiments are carried out on Ti6Al4V. Experiments were
conducted by varying the peak current, pulse on time and pulse off time. A mathematical model is developed to correlate the influences of these variables and material removal rate of workpiece. Design of
experiments (DOE) method and response surface methodology
(RSM) techniques are implemented. The validity test of the fit and adequacy of the proposed models has been carried out through
analysis of variance (ANOVA). The obtained results evidence that as
the material removal rate increases as peak current and pulse on time
increases. The effect of pulse off time on MRR changes with peak ampere. The optimum machining conditions in favor of material removal rate are verified and compared. The optimum machining
conditions in favor of material removal rate are estimated and verified with proposed optimized results. It is observed that the developed model is within the limits of the agreeable error (about
4%) when compared to experimental results. This result leads to desirable material removal rate and economical industrial machining to optimize the input parameters.
Abstract: Conventionally the selection of parameters depends
intensely on the operator-s experience or conservative technological
data provided by the EDM equipment manufacturers that assign
inconsistent machining performance. The parameter settings given by
the manufacturers are only relevant with common steel grades. A
single parameter change influences the process in a complex way.
Hence, the present research proposes artificial neural network (ANN)
models for the prediction of surface roughness on first commenced
Ti-15-3 alloy in electrical discharge machining (EDM) process. The
proposed models use peak current, pulse on time, pulse off time and
servo voltage as input parameters. Multilayer perceptron (MLP) with
three hidden layer feedforward networks are applied. An assessment
is carried out with the models of distinct hidden layer. Training of the
models is performed with data from an extensive series of
experiments utilizing copper electrode as positive polarity. The
predictions based on the above developed models have been verified
with another set of experiments and are found to be in good
agreement with the experimental results. Beside this they can be
exercised as precious tools for the process planning for EDM.
Abstract: In this paper, a new hybrid of genetic algorithm (GA)
and simulated annealing (SA), referred to as GSA, is presented. In
this algorithm, SA is incorporated into GA to escape from local
optima. The concept of hierarchical parallel GA is employed to
parallelize GSA for the optimization of multimodal functions. In
addition, multi-niche crowding is used to maintain the diversity in
the population of the parallel GSA (PGSA). The performance of the
proposed algorithms is evaluated against a standard set of multimodal
benchmark functions. The multi-niche crowding PGSA and normal
PGSA show some remarkable improvement in comparison with the
conventional parallel genetic algorithm and the breeder genetic
algorithm (BGA).
Abstract: The group mutual exclusion (GME) problem is an
interesting generalization of the mutual exclusion problem. In the
group mutual exclusion, multiple processes can enter a critical
section simultaneously if they belong to the same group. In the
extended group mutual exclusion, each process is a member of
multiple groups at the same time. As a result, after the process by
selecting a group enter critical section, other processes can select the
same group with its belonging group and can enter critical section at
the moment, so that it avoids their unnecessary blocking. This paper
presents a quorum-based distributed algorithm for the extended
group mutual exclusion problem. The message complexity of our
algorithm is O(4Q ) in the best case and O(5Q) in the worst case,
where Q is a quorum size.
Abstract: The present contribution deals with the
thermophoretic deposition of nanoparticles over a rapidly rotating
permeable disk in the presence of partial slip, magnetic field, thermal
radiation, thermal-diffusion, and diffusion-thermo effects. The
governing nonlinear partial differential equations such as continuity,
momentum, energy and concentration are transformed into nonlinear
ordinary differential equations using similarity analysis, and the
solutions are obtained through the very efficient computer algebra
software MATLAB. Graphical results for non-dimensional
concentration and temperature profiles including thermophoretic
deposition velocity and Stanton number (thermophoretic deposition
flux) in tabular forms are presented for a range of values of the
parameters characterizing the flow field. It is observed that slip
mechanism, thermal-diffusion, diffusion-thermo, magnetic field and
radiation significantly control the thermophoretic particles deposition
rate. The obtained results may be useful to many industrial and
engineering applications.