Abstract: This paper presents an experimental investigation on the optimization and the effect of the cutting parameters on Material Removal Rate (MRR) in Plasma Arc Cutting (PAC) of EN-45A Material using Taguchi L 16 orthogonal array method. Four process variables viz. cutting speed, current, stand-off-distance and plasma gas pressure have been considered for this experimental work. Analysis of variance (ANOVA) has been performed to get the percentage contribution of each process parameter for the response variable i.e. MRR. Based on ANOVA, it has been observed that the cutting speed, current and the plasma gas pressure are the major influencing factors that affect the response variable. Confirmation test based on optimal setting shows the better agreement with the predicted values.
Abstract: In the present work response surface methodology (RSM) based central composite design (CCD) is used for analyzing the electrical discharge machining (EDM) process. For experimentation, mild steel is selected as work piece and copper is used as electrode. Three machining parameters namely current (I), spark on time (Ton) and spark off time (Toff) are selected as the input variables. The output or response chosen is material removal rate (MRR) which is to be maximized. To reduce the number of runs face centered central composite design (FCCCD) was used. ANOVA was used to determine the significance of parameter and interactions. The suitability of model is tested using Anderson darling (AD) plot. The results conclude that different parameters considered i.e. current, pulse on and pulse off time; all have dominant effect on the MRR. At last, the optimized parameter setting for maximizing MRR is found through main effect plot analysis.
Abstract: Productivity and quality are two important aspects that have become great concerns in today’s competitive global market. Every production/manufacturing unit mainly focuses on these areas in relation to the process, as well as the product developed. The electrical discharge machining (EDM) process, even now it is an experience process, wherein the selected parameters are still often far from the maximum, and at the same time selecting optimization parameters is costly and time consuming. Material Removal Rate (MRR) during the process has been considered as a productivity estimate with the aim to maximize it, with an intention of minimizing surface roughness taken as most important output parameter. These two opposites in nature requirements have been simultaneously satisfied by selecting an optimal process environment (optimal parameter setting). Objective function is obtained by Regression Analysis and Analysis of Variance. Then objective function is optimized using Genetic Algorithm technique. The model is shown to be effective; MRR and Surface Roughness improved using optimized machining parameters.
Abstract: Wire Electric Discharge Machining (WEDM) is
thermal machining process capable of machining very hard
electrically conductive material irrespective of their hardness.
WEDM is being widely used to machine micro scale parts with the
high dimensional accuracy and surface finish. The objective of this
paper is to optimize the process parameters of wire EDM to fabricate
the micro channels and to calculate the surface finish and material
removal rate of micro channels fabricated using wire EDM. The
material used is aluminum 6061 alloy. The experiments were
performed using CNC wire cut electric discharge machine. The effect
of various parameters of WEDM like pulse on time (TON) with the
levels (100, 150, 200), pulse off time (TOFF) with the levels (25, 35,
45) and current (IP) with the levels (105, 110, 115) were investigated
to study the effect on output parameter i.e. Surface Roughness and
Material Removal Rate (MRR). Each experiment was conducted
under different conditions of pulse on time, pulse off time and peak
current. For material removal rate, TON and Ip
were the most significant process parameter. MRR increases with the increase in
TON and Ip and decreases with the increase in TOFF. For surface
roughness, TON and Ip have the maximum effect and TOFF was found
out to be less effective.
Abstract: In this study, a multi objective optimization for end
milling of Al 6061 alloy has been presented to provide better
surface quality and higher Material Removal Rate (MRR). The input
parameters considered for the analysis are spindle speed, depth of cut
and feed. The experiments were planned as per Taguchis design of
experiment, with L27 orthogonal array. The Grey Relational Analysis
(GRA) has been used for transforming multiple quality responses
into a single response and the weights of the each performance
characteristics are determined by employing the Principal Component
Analysis (PCA), so that their relative importance can be properly and
objectively described. The results reveal that Taguchi based G-PCA
can effectively acquire the optimal combination of cutting parameters.
Abstract: Several researches have been conducted to study
consumption of energy in cutting process. Most of these researches
are focusing to measure the consumption and propose consumption
reduction methods. In this work, the relation between the cutting
parameters and the consumption is investigated in order to establish a
generalized energy consumption model that can be used for process
and production planning in real production lines. Using the
generalized model, the process planning will be carried out by taking
into account the energy as a function of the selected process
parameters. Similarly, the generalized model can be used in
production planning to select the right operational parameters like
batch sizes, routing, buffer size, etc. in a production line. The
description and derivation of the model as well as a case study are
given in this paper to illustrate the applicability and validity of the
model.
Abstract: In the literature, Improved Recycling Folded Cascode (IRFC) Operational Transconductance Amplifier (OTA) is proposed for enhancing the DC gain and the Unity Gain Bandwidth (UGB) of the Recycling Folded Cascode (RFC) OTA. In this paper, an enhanced IRFC (EIRFC) OTA which uses positive feedback at the cascode node is proposed for enhancing the differential mode (DM) gain without changing the unity gain bandwidth (UGB) and lowering the Common mode (CM) gain. For the purpose of comparison, IRFC and EIRFC OTAs are implemented using UMC 90nm CMOS technology and studied through simulation. From the simulation, it is found that the DM gain and CM gain of EIRFC OTA is higher by 6dB and lower by 38dB respectively, compared to that of IRFC OTA for the same power and area. The slew rate of EIRFC OTA is also higher by a factor of 1.5.
Abstract: This paper reports the optimal process conditions for machining of three different types of MMC’s 65vol%SiC/A356.2; 10vol%SiC-5vol%quartz/Al and 30vol%SiC/A359 using PMEDM process. MRR, TWR, SR and surface integrity were evaluated after each trial and contributing process parameters were identified. The four responses were then collectively optimized using TOPSIS and optimal process conditions were identified for each type of MMC. The density of reinforced particles shields the matrix material from spark energy hence the high MRR and SR was observed with lowest reinforced particle. TWR was highest with Cu-Gr electrode due to disintegration of the weakly bonded particles in the composite electrode. Each workpiece was examined for surface integrity and ranked as per severity of surface defects observed and their rankings were used for arriving at the most optimal process settings for each workpiece.
Abstract: This paper is intended to develop an artificial neural network (ANN) based model of material removal rate (MRR) in the turning of ferrous and nonferrous material in a Indian small-scale industry. MRR of the formulated model was proved with the testing data and artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between inputs and output parameters during the turning of ferrous and nonferrous materials. The input parameters of this model are operator, work-piece, cutting process, cutting tool, machine and the environment.
The ANN model consists of a three layered feedforward back propagation neural network. The network is trained with pairs of independent/dependent datasets generated when machining ferrous and nonferrous material. A very good performance of the neural network, in terms of contract with experimental data, was achieved. The model may be used for the testing and forecast of the complex relationship between dependent and the independent parameters in turning operations.
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: This paper proposes a study of input impedance of 2 types of CMOS active inductors. It derives 2 input impedance formulas. The first formula is the input impedance of the grounded active inductor. The second formula is the input impedance of the floating active inductor. After that, these formulas can be used to simulate magnitude and phase response of input impedance as a function of current consumption with MATLAB. Common mode rejection ratio (CMRR) of the fully differential bandpass amplifier is derived based on superposition principle. CMRR as a function of input frequency is plotted as a function of current consumption.
Abstract: Metal matrix composites (MMC) are generating
extensive interest in diverse fields like defense, aerospace, electronics
and automotive industries. In this present investigation, material
removal rate (MRR) modeling has been carried out using an
axisymmetric model of Al-SiC composite during electrical discharge
machining (EDM). A FEA model of single spark EDM was
developed to calculate the temperature distribution.Further, single
spark model was extended to simulate the second discharge. For
multi-discharge machining material removal was calculated by
calculating the number of pulses. Validation of model has been done
by comparing the experimental results obtained under the same
process parameters with the analytical results. A good agreement was
found between the experimental results and the theoretical value.
Abstract: The operating control parameters of injection
flushing type of electrical discharge machining process on stainless
steel 304 workpiece with copper tools are being optimized
according to its individual machining characteristic i.e. material
removal rate (MRR). Lower MRR during EDM machining process
may decrease its- machining productivity. Hence, the quality
characteristic for MRR is set to higher-the-better to achieve the
optimum machining productivity. Taguchi method has been used
for the construction, layout and analysis of the experiment for each
of the machining characteristic for the MRR. The use of Taguchi
method in the experiment saves a lot of time and cost of preparing
and machining the experiment samples. Therefore, an L18
Orthogonal array which was the fundamental component in the
statistical design of experiments has been used to plan the
experiments and Analysis of Variance (ANOVA) is used to
determine the optimum machining parameters for this machining
characteristic. The control parameters selected for this
optimization experiments are polarity, pulse on duration, discharge
current, discharge voltage, machining depth, machining diameter
and dielectric liquid pressure. The result had shown that the higher
the discharge voltage, the higher will be the MRR.
Abstract: Greenhouse gases (GHG) emissions impose major
threat to global warming potential (GWP). Unfortunately
manufacturing sector is one of the major sources that contribute
towards the rapid increase in greenhouse gases (GHG) emissions. In
manufacturing sector electric power consumption is the major driver
that influences CO2 emission. Titanium alloys are widely utilized in
aerospace, automotive and petrochemical sectors because of their
high strength to weight ratio and corrosion resistance. Titanium
alloys are termed as difficult to cut materials because of their poor
machinability rating. The present study analyzes energy consumption
during cutting with reference to material removal rate (MRR).
Surface roughness was also measured in order to optimize energy
consumption.
Abstract: FW4 is a newly developed hot die material widely
used in Forging Dies manufacturing. The right selection of the
machining conditions is one of the most important aspects to take
into consideration in the Electrical Discharge Machining (EDM) of
FW4. In this paper an attempt has been made to develop
mathematical models for relating the Material Removal Rate (MRR),
Tool Wear Ratio (TWR) and surface roughness (Ra) to machining
parameters (current, pulse-on time and voltage). Furthermore, a study
was carried out to analyze the effects of machining parameters in
respect of listed technological characteristics. The results of analysis
of variance (ANOVA) indicate that the proposed mathematical
models, can adequately describe the performance within the limits of
the factors being studied.
Abstract: The use of hard and brittle material has become
increasingly more extensive in recent years. Therefore processing of
these materials for the parts fabrication has become a challenging
problem. However, it is time-consuming to machine the hard brittle
materials with the traditional metal-cutting technique that uses
abrasive wheels. In addition, the tool would suffer excessive wear as
well. However, if ultrasonic energy is applied to the machining
process and coupled with the use of hard abrasive grits, hard and
brittle materials can be effectively machined. Ultrasonic machining
process is mostly used for the brittle materials. The present research
work has developed models using finite element approach to predict
the mechanical stresses sand strains produced in the tool during
ultrasonic machining process. Also the flow behavior of abrasive
slurry coming out of the nozzle has been studied for simulation using
ANSYS CFX module. The different abrasives of different grit sizes
have been used for the experimentation work.
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: Industrial surveys shows that manufacturing
companies define the qualities of thermal removing process based on
the dimension and physical appearance of the cutting material
surface. Therefore, the roughness of the surface area of the material
cut by the plasma arc cutting process and the rate of the removed
material by the manual plasma arc cutting machine was importantly
considered. Plasma arc cutter Selco Genesis 90 was used to cut
Standard AISI 1017 Steel of 200 mm x100 mm x 6 mm manually
based on the selected parameters setting. The material removal rate
(MRR) was measured by determining the weight of the specimens
before and after the cutting process. The surface roughness (SR)
analysis was conducted using Mitutoyo CS-3100 to determine the
average roughness value (Ra). Taguchi method was utilized to
achieve optimum condition for both outputs studied. The
microstructure analysis in the region of the cutting surface is
performed using SEM. The results reveal that the SR values are
inversely proportional to the MRR values. The quality of the surface
roughness depends on the dross peak that occurred after the cutting
process.
Abstract: This paper addresses modeling and optimization of process parameters in powder mixed electrical discharge machining (PMEDM). The process output characteristics include metal removal rate (MRR) and electrode wear rate (EWR). Grain size of Aluminum powder (S), concentration of the powder (C), discharge current (I) pulse on time (T) are chosen as control variables to study the process performance. The experimental results are used to develop the regression models based on second order polynomial equations for the different process characteristics. Then, a genetic algorithm (GA) has been employed to determine optimal process parameters for any desired output values of machining characteristics.
Abstract: In this paper, we proposed the distribution of mesh
normal vector direction as a feature descriptor of a 3D model. A
normal vector shows the entire shape of a model well. The
distribution of normal vectors was sampled in proportion to each
polygon's area so that the information on the surface with less surface
area may be less reflected on composing a feature descriptor in order
to enhance retrieval performance. At the analysis result of ANMRR,
the enhancement of approx. 12.4%~34.7% compared to the existing
method has also been indicated.