29 chapter-2 - information and library network...
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29
Chapter-2
LITERATURE SURVEY
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30
CHAPTER - 2
LITERATURE SURVEY S.No. Name of the Sub-Title Page No.
2.1 Overview 31 2.2 Welding and Allied Processes 32 2.3 Conventional and Non-Conventional Machining Processes 41 2.4 Other Productive and Non-Productive Processes 50
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31
LITERATURE SURVEY 2.1 Overview
An extensive survey of the existing literature has was carried
out to uncover the progress of research in the focal area and in the
direction of the present work.
The quality of welding depends on the combination of various
process parameters that depends on the selection of the process
parameters. In robot welding, the welding path is taught to the robot
by a teach pendant and the weld quality is further improved by
selecting the appropriate process parameter values by trial and error
method. The selection of optimum process variables is possible by two
methods. The first method is an empirical method that takes into
account the experience of welders and welding engineers. This method
is inadequate to explain the relationship between the input and the
output of the process. The second method is based on the design of
experiments and mathematical models. In this method, it is possible
to relate the output variables (quality characteristics) and the input
variables (process variables) based on scientific and statistical
grounds.
The literature survey reveals studies and investigation projects
carried out as per the second method of optimization, i.e., selecting
the process variables for the intended application in welding and other
manufacturing processes.
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32 The review of literature reveals that the optimization of process
parameters has been carried out for various manufacturing processes
like casting, machining, welding and even for non- productive
processes like consumer electronics. The welding parameters have
been optimized for various objectives like strength, bead dimensions,
penetration, distortion and other similar process quality objectives. An
appreciable amount of research work has been published in the
production of leak-free welding joints; however, not much work has
been done to understand the optimization of welding parameters to
produce leak-free joints.
Taguchi method provide an efficient and systematic way to
optimize the design for performance, quality and cost. Taguchi
methods have been used successfully in Japan and the United States
in automobile and consumer electronic sectors [25]. The literature
survey presented here is grouped under the following sub-headings.
i. Welding and allied processes
ii. Conventional and non-conventional machining processes
iii. Other productive and non-productive processes
2.2 Welding and Allied Processes
Naidu et al. [26] have made a statement that the modeling of a
welding process generally means the derivation of a set of
mathematical equations describing the physical process by means of
the fundamental principles of scientific or statistical or experimental
techniques. The model describes the behavior of the system to be
controlled.
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33 Kumanan and Dhas [27] revealed that technologies could be
utilized to measure, control and optimize the Submerged Arc Welding
(SAW) process parameters to obtain a good quality of weld. They have
used multiple regression analysis, artificial neural network and
response surface methodology to model the process.
Shiva et al. [28] proposed a model for plasma transferred arc
surfacing. The model describes welding current, oscillation width,
travel speed, pre-heat temperature, power, feed rate and response
variable penetration. In the study, the central composite rotatable
design was used to conduct the experiments.
Bolinger [29] reported that welding is more complex than any
other manufacturing processes, as it involves complex interactions of
solid, liquid, gaseous and plasma state phenomena. Further, the
entire process takes place simultaneously in relatively small volumes
and over a short period of time. Thus, to relate the inputs and the
outputs of the welding process, the scientific means of measuring,
controlling and optimizing the variables are essential to exploit the
welding technology.
Jayaram and Siva [30] reported the use of Taguchi methods to
optimize the stir welding process parameters to weld aluminum alloy
A319. The parameters, such as rotational speed, welding speed and
axial force, were optimized to achieve the maximum tensile strength at
three levels for each parameter. Experiments were conducted as per
the L8 Orthogonal Array and Analysis of Variance (ANOVA) was also
applied to determine the contribution of each parameter.
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34 Kannan and Murugan [31], reported that stainless steel
cladding by flux cored arc welding contains some amount of delta
ferrite and the control of which is very essential, else it could lead to
inferior mechanical and corrosive resistance properties. To reduce
delta ferrite, the authors had optimized the process by the central
composite rotatable design. The process parameters were welding
speed, distance between nozzle and plate, welding current and torch
angle - with each parameter having five levels. Mathematical models
were developed by using regression coefficients, coefficients are
calculated by using the six sigma concept and were verified by the
Systat software.
Although Resistance Spot Welding (RSW) is a well-established
process in the automotive industry, according to Cho and Hu [32] a
serious concern exists with the welding of aluminum by RSW as it has
high conductivity, narrow range of temperature between solidus and
liquidus state, low melting point and non-conductive film on the
surface. The authors overcame the challenges by using robust
operating parameters obtained from the design of experiments.
Mathematical models were also developed to predict the results.
Ericson and Nylen [33] optimized robot welding speed to get
complete penetration of welding joint. Optimization is not done by
actual conduction, instead it is done by integrating robot simulation,
finite element analysis and numerical optimization.
Vel and Gunaraj [34] developed a mathematical model to
predict the angular distortion of the welding of steel plates. The
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35 coefficients of the model were tested for significance and the model
was validated by drawing curves for the predicted and the actual
values of distortion.
Amar et al. [35] applied the Taguchi methods to optimize the
SAW process parameters for the hard facing process. The welding
process parameters were arc current, arc voltage, welding speed,
electrode stick-out and pre-heat temperature. Orthogonal array L27
(313) was used. The parameters were optimized for deposition rate,
dilution and hardness. Interactions between arc current, arc voltage
and welding speed were studied for all the three objectives.
Kim et al. [36] in their investigation, applied controlled random
search to optimize Gas Metal Arc (GMA) welding for complete
penetration of 4 mm thick mild steel plates.
In order to obtain the optimal tensile properties, Bala et al. [37]
investigated the Tungsten Inert Gas (TIG) welding process parameters.
The authors used the Taguchi technique to study all the parameters’
range using few experiments. Orthogonal array L16 (44) was adopted
with the process parameters peak current, base current, pulse
frequency and pulse time. Each parameter range was split into four
levels and 16 experiments were conducted as per the orthogonal
array. However, interactions between the process parameters were not
considered. ANOVA tool was also used to identify the factor, which
significantly affected the tensile properties.
Ghen et al. [38] optimized the process parameters of laser
engraving by using the Taguchi fractional factorial experiments. The
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36 engraving was done on glass substrate coated with a film. The selected
process parameters were beam expansion ratio, focal length, laser
power, pulse repetition rate and speed of engraving. The objective of
this experimentation was to get an engraved line with a minimum
width of 18μm. L16 (24) OA was used and no interactions were studied.
Signal to Noise ratio (S/N) for the response was also calculated using
lower-the-best characteristic. ANOVA was also used to calculate the
contribution of each parameter.
Gunaraj and Murugan [39] optimized the SAW process
parameters to get an optimal weld bead volume. In this study, the
central composite rotatable design was adopted. The selected
parameters were welding voltage, wire feed rate, welding speed and
nozzle to plate distance. A mathematical model was developed to
predict the weld bead dimensions by the use of MATLAB. Welding was
performed on pipes without considering the interactions.
Mishra and Debroy [40] developed six feed-forward neural
networks, which were based on genetic algorithms and gradient-
descent for Gas Tungsten Arc (GTA) welding. To develop the neural
network models, 17 input variables, which included the welding
process parameters and the important material properties, were
considered,. The output variables of the models were depth, width,
length of the weld pool, peak temperature, cooling time and minimum
liquid velocity in the weld pool.
Palaniradja et al. [41] analyzed the influence of various process
variables of gas carburizing on the surface hardness. The variables
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37 selected were holding time, carbon potential, carburizing temperature
and quenching time. For each parameter, three levels were fixed.
Based on the number of factors and their levels, L9 (34) orthogonal
array was selected and ANOVA was also applied to determine the
influence of parameters. It was found that the percentage of
contribution in holding time was 56%, indicating that quenching time
had the most influence on surface hardness.
Ic et al. [42] conducted 23 full factorial experiments to study
responses, such as yield point, tension or compression strength and
modulus of elasticity of GMA welding. The process variables were
welding groove angle, groove geometry and type of load on welding.
For all these responses, mathematical models were developed using
the Minitab software.
Ramasamy et al. [43] employed design of experiments approach
to study the influence of factors of drawn arc welding of steel sheets.
The chief quality responses used for the study were tensile strength,
shear strength, torque to failure and bending angle to failure. In this
investigation, the authors had placed more importance on general
manufacturing variables than the inherent process variables. The
process variables selected for the examination were polarity, type of
coating, thickness of material, stud design, stud coating, collet wear,
power type and surface condition. A 32- trial of design of experiments
was selected with two interactions. The effects of factors were
estimated by ANOVA, and the regression models were developed for all
of the four chief quality responses.
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38 Sapakal and Telsang [44] analyzed operating or process
parameters of Metal Inert Gas welding to attain full depth of
penetration. In this study they studied the influence process
parameters on penetration. For the investigation, the selected
parameters were welding current, welding voltage and welding speed.
Each parameter range was split into three meaningful levels. Based
on number of factors and their levels L9 OA was selected. No
interactions were considered, however ANOVA computations were
calculated to estimate contribution of factors. It is found that
contribution of welding voltage was higher (84.42%) than welding
speed (6.83%) and welding current (3.84%). Test experiments were
also conducted to confirm the results.
Siva et al. [45] observed that variation in quality can be
controlled by making a robust process. As the variation of quality is
considerably higher in welding than in any other manufacturing
process, the application of a robust design of parameters improves the
quality of welding.
Companelli et al. [46] studied the effect of friction spot welding
parameters such as rotational speed, plunge depth and dwell time on
the lap shear strength of AZ31 magnesium alloy joints. The
optimization of the input process parameters was carried out by the
use of the L9 OA. ANOVA computations was used to estimate the effect
of each factor. It was found that the contribution of plunge depth was
more (57.48%) compared to other factors.
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39 Haragopal et al. [47] employed the Taguchi method to design
process parameters that optimize the mechanical properties of
aluminum alloy used for construction of aerospace wings. The
selected parameters of the Metal Inert Gas (MIG) welding process were
pressure, current, grove angle and pre-heating. Experiments were
conducted as per the L9 OA. ANOVA computations revealed that
current was the most influencing parameter on the ultimate tensile
strength with a contribution of 57.5% at 99% confidence level.
Ramirez and Johnson [48] studied the effects of arc length,
welding current, welding length and conditions of the cellulose
electrode (as received or dried) on the enrichment of alloying elements
in the deposited weld metal. Experiments were not conducted as per
any OA, but were instead conducted in three sets. The outcome of this
investigation was that the arc length, weld length (time) and dried
condition of the electrode had a primary effect on the chemical
composition of the deposited weld metal.
Purohit and Digamber [49] employed the L25 OA to optimize the
process parameters of SAW, in order to to obtain the desired bead
dimensions. The S/N ratio were calculated to understand the process
and the contributions of various factors like welding current, arc
voltage, welding speed and nozzle to plate distance.
Ismail and Taha [50] employed the Taguchi optimization
technique to optimize the process parameters of plasma arc surface
hardening process. The selected parameters were arc current,
scanning velocity and carbon content. Experiments were conducted as
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40 per the L8 OA. Optimum parameters were decided by the aid of the
S/N ratio and ANOVA computations.
Vinod [51] optimized the SAW process parameters by using the
Taguchi technique. The selected parameters were voltage, current,
welding speed and basicity index. ANOVA was applied to find the
contribution of various parameters, significance was also tested by t-
test. Regression models for all objective functions were developed and
validations of the models were also done.
Pawan et al. [52] optimized the GTA welding process parameters
to weld aluminum alloys. Five parameters were selected and
experiments were conducted as per the L25 OA.
Thakur and Nandedkar [53] optimized the process parameters
of RSW. The selected parameters were pressure, welding time and
current. The objective function was to maximize the tensile strength.
Experiments were conducted as per the L27 OA and the work piece
was Stainless Steel AISI304.
Antonio et al. [54] optimized the process parameters of MIG
welding. The selected parameters were voltage, speed, arc oscillation,
gun orientation, shielding gas and filler metal. The objectives were to
evaluate the percentage of dilution and ratio between reinforcement
and bead width. Experiments were conducted as per the L9 OA.
Rao et al. [55] attempted to find a solution to the alloying of
RSW sheets with electrode and the subsequent deterioration of
welding quality and the wear of electrode. This problem was
minimized by optimizing the shear strength of RSW sheets. For this,
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41 the Taguchi optimization technique was employed. The selected
parameters were welding current, weld time, electrode diameter and
welding force. Experiments were conducted as per the L27 OA and the
optimum parameters were decided with the help of ANOVA.
Edwin [56] optimized the process parameters for SAW to obtain
the required bead dimensions. For this investigation, the L9 OA was
used and the selected parameters were welding current, arc voltage,
welding speed and electrode stick. Multiple regression models were
developed to predict the quality of weld.
Catarina and Virginia et al. [57] optimized the process
parameters for improvement of friction stir welded joints of the
aerospace aluminum alloy. The authors had employed the Taguchi
method for optimization of the selected parameters, namely, forging
force, travel speed and pin length.
Sathiya and Abdul [58] employed the grey-based Taguchi
method to optimize the laser welding parameters with the objective of
reaching the required bead geometry.
Dhas and Kumanan [59] optimized the parameters of SAW to
obtain consistent weld quality. For this investigation, they employed
the Taguchi technique. The selected parameters were welding current,
arc voltage, welding speed and electrode stick. Multiple regression
model was developed to predict the quality of weld.
2.3 Conventional and Non-Conventional Machining Processes
Harish and Pradeep [60] applied the L27 (313) OA to minimize the
feed force. The selected parameters were cutting speed, feed rate and
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42 depth of cut and the interaction between the process parameters.
Experiments were repeated thrice. The S/N ratios of “lower-the–better”
type were calculated for all the 27 runs. ANOVA estimated the
contributions of various parameters. The contribution of depth of cut
was 55.15%, feed rate was 23.33% and cutting speed was 2.63%, at
95% confidence level.
Gopal et al. [61] applied the Taguchi optimization technique to
optimize the process parameters of Drill-Reamer operation. The
objective was to improve the surface finish. The L8 orthogonal array
was selected with three operating parameters. They were cutting
speed, depth of cut and depth of feed. Each parameter had two levels.
The Drill-Reamer operations were carried out for mild steel, aluminum
and cast Iron. Three replications were conducted for all the three
materials and the average response was calculated. To study the
significance and the contributions, ANOVA computations were also
performed. The results of the investigations were as follows: the
aluminum depth of cut had almost 50% influence on the surface
finish, followed by cutting speed and feed rate. For cast Iron, the
influence of both cutting speed and feed rate was relatively high. For
mild steel, the cutting speed had high influence on the surface finish.
Cha-Lieh Yang [62] applied the Taguchi method to optimize the
glass fiber cutting process. The objective was to reduce the wear of
high Carbon steel blades. The L9 (34) orthogonal array was selected
without interactions. The operating parameters were cutting speed,
cutting volume and cutting load and an error term was also
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43 considered as a parameter. Experiments were conducted as per the
OA L9 and the average wear of the cutting blade was measured by
Scanned Electron Microscope and the S/N ratio for the same had been
calculated. The contributions of the process parameters were
estimated by ANOVA. The cutting speed had an influence of 50%,
followed by cutting volume and cutting load.
Bala et al. [63] selected the Taguchi L18 OA to analyze the
experimental result of machining with respect to the surface finish.
CNC milling was employed to perform experiments for ball end mill of
diameter 10 mm. The selected operating parameters were cutting
speed, feed, depth and width of cut. No interactions were considered
between the process parameters. Along with the evaluation of surface
finish, the tool wear and tool life were also estimated. The S/N ratios
for all the objectives had been calculated. ANOVA was applied in the
investigation to find out the influence of various parameters on the
objective function. ANOVA computations indicated that speed was
more influential than any other parameter. A multiple regression
models were developed to relate the outputs and inputs and also to
predict results.
Yin-fong Tzeng et al. [64] proposed the Taguchi method to
optimize the process parameters with the objective of improving the
machining efficiency and geometrical accuracy. The machine
employed for the investigation was FEELER QM-22 CNC precision
three axis milling machine. The work was planned in two phases. In
the first phase, the machining efficiency was optimized by conducting
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44 experiments as per the L8 OA. The process parameters were milling
type, cutting speed, feed per tooth, film material, number of teeth,
rake angle and helix angle. ANOVA computations revealed that the
combined contribution of the control factors of cutting speed, number
of teeth and feed was 82.63%. The contribution of the noise factor was
only 0.328%. In the second phase, a linear regression model was
developed between the programmed dimensions and the actual
dimensions produced in the experiments of the first phase.
Soma and Swamy [65] optimized the turning operation
parameters to get the optimal surface roughness. The operating
parameters selected were speed (A), feed (B) and depth of cut (C). For
each parameter, the range was split into three levels. Based on the
number of factors and the number of levels, the L9 OA was selected.
The details of the experiments were CNC lathe, AL 6351 work material
with 75mm length and 25.3mm diameter dimensions. Using the
Minitab software, a model was developed for experimental purpose.
Ra = 1.69+0.00146 A -1.92 B +0.24 C --- --Eq. (2.1)
Pragnesh et al. [66] investigated the effect of machining factors
on both the surface finish and the power consumption. For the
investigation, the full factorial design of experiments was adopted. The
machining was performed on 6063 aluminum composite with
Titanium Carbide (TiC) reinforcement. The engine lathe and the
cutting tool machines were used. These machines contained
polycrystalline diamond insert of fine grade. The selected parameters
were cutting speed, feed rate and depth of cut. Each parameter range
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45 was split into three meaningful levels. Based on 33, 27 trails were
conducted and objective quality responses, surface roughness and
power consumed were measured. An ANOVA computation of the result
revealed the influence of various parameters on surface roughness
and power consumption.
Danial et al. [67] proposed the design of experiment method to
maximize the material removal rate (MRR) in Wire Electrical Discharge
Machining. The selected parameters were pulse time, pulse off time
and peak current. Each parameter was split into three levels.
Experiments were conducted as per the central composite design.
ANOVA computations signified peak current for MRR and the
estimated contribution was 57.65%.
Venkataramana et al. [68] optimized the machining operation
parameters to get adequate surface finish. The selected control factors
were cutting speed, feed and depth of cut - each factor with three
levels. Based on the factors and their levels, the OA L9 was selected for
conducting the experiment.
Surinder et al. [69] employed the L8 OA to evaluate the influence
of cutting parameter on the cutting performance of Unidirectional
Glass Fiber Reinforced Plastics (UD-GFRP). The selected parameters
were cutting speed (A), feed rate (B), depth of cut (C) and interactions
AxB, AxC and BxC. The contributions of various parameters were
calculated by using ANOVA. The contribution of depth of cut was
53.87%, whereas other parameters’ contributions were relatively less.
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46 Neeraj and Rajesh [70] studied the effect of various process
parameters of Wire Electric Discharge Machining, such as pulse
width, time between two pulses, maximum feed rate, voltage, short
pulse and wire mechanical tension on surface roughness, while
machining cryogenic treated D-3 material. Experiments were
conducted as per the L27 OA; and, ANOVA computations were also
calculated to know the significance and contribution of the process
parameters.
Sahil et al. [71] investigated the cutting parameters of surface
finish of EN-8 steel using CNC turning machine. In this case also, the
selected parameters were cutting speed, feed and depth of cut. The
ANOVA results revealed that the influence of cutting speed was higher
(54.74%) than other parameters.
Shanti et al. [72] adopted the L27 (313) OA to investigate the
effect of drilling operating parameters on burr height. The parameters
selected were cutting speed, feed rate and point angle. The S/N ratio
of “smaller-the-best” type was also calculated.
Irfan et al. [73] performed experimental investigation work
similar work of the Shanti Prakash et al., The authors had used
Acrylic sheet, in contrast to Prakash’s work material that was steel.
Chaudhari et al. [74] utilized Taguchi's L9 OA to analyze the
effect of cutting parameters on the MRR and surface roughness. The
selected parameters were cutting speed, feed and depth of cut.
Izzet Karakut et al. [75] studied the effects of the process
parameters on the Kerf angle. Taguchi's L16 OA was employed to
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47 conduct the experiments. The process parameters were traverse
speed, abrasive flow rate, standoff distance, water pressure and
abrasive size. ANOVA computations revealed that the percentage
contribution of traverse speed was high at about 35% and the stand
off distance was 33%.
Pai et al. [76] investigated the effects of the grinding process
parameters on the metal removal rate and the surface roughness on
surface grinding of AL-6061-SiC composite using Taguchi's Design Of
Experiments. The selected parameters were percentage of SiC, feed
and depth of cut. Experiments were conducted as per the L27 OA.
ANOVA calculations were performed to estimate the contribution of
the process parameters.
Ravinder [77] studied the effect of the process parameters on
ceramic coating by cold spraying. The selected process parameters
were gas inlet pressure, gas inlet temperature, particle diameters and
particle temperature. Experiments were conducted as per the L16 OA.
ANOVA computations were performed to estimate the contribution of
the process parameters.
Patel [78] have done investigations similar to Bala Murugan
[63], in which the material was AL 6351-T6 and the machine used was
BFW V-30 CNC vertical milling machine.
Asafa and Fadare [79] optimized the cutting parameters with the
objectives of reducing flank wear and nose wear. A model was also
developed using MATLAB neural network tool box.
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48 Marimuthu and Chandrasekaran [80] optimized the cutting
parameters of Fanuc CNC machine to machine stainless steel, with
the objective of reducing tool wear and surface roughness. The cutting
tool used was multi-layered CNMG 120408 coated with TiCN+ AL2O3
of 14 µm. The selected parameters were cutting speed, feed and depth
of cut. Experiments were conducted as per the L27 OA.
Ashok and Bidyadhar [81] conducted experimental studies on
turning using coated carbide insert. The process was optimized by
employing Taguchi’s L27 orthogonal array. The objective of the
optimization was to predict the surface roughness. The cutting
parameters used were cutting speed, feed and depth of cut. Models
required for the experimental conditions were developed by using the
response surface methodology.
Rama Rao et al. [82] optimized the process parameters of
chemical machining to machine LM6 aluminum composites. The
selected parameters were voltage, feed rate and electrolyte
concentration. Experiments were conducted as per the L9 OA. The
objective function of this investigation was to improve the MRR. The
contributions of various parameters were calculated by ANOVA. The
contribution of feed rate was 58% and the contribution of other
parameters was relatively low. An association between the outputs
and the inputs was investigated through the regression model.
Ballal et al. [83] employed the Taguchi L27 OA to study the
effects of machining parameters on surface finish, tool wear and MRR,
in the turning of cast Iron (FG260) brake drum.
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49 Kamaraj et al. [84] optimized the parameters of the machining
process to machine Martensitic Stainless Steel (AISI410). The L27 OA
was selected, based on the parameters and their levels. The
parameters were cutting speed, feed rate, depth of cut and cutting
tool. ANOVA computations were performed to estimate the significance
of the operating parameters.
Anil and Jyoti [85] applied the L9 OA to optimize the end milling
process parameters to machine mild steel. The selected process
parameters were spindle speed, feed rate, depth of cut and width of
cut. The main objective was to estimate the removal rate of material
and surface finish.
Swaraj and Bindu [86] optimized the Micro-EDM process
parameters. For this, they adopted the L18 OA and selected polarity,
speed of electrode, peak current and pulse time as the important
operating parameters.
Bharat et al. [87] optimized the process parameters of CNC
milling operation. The L25 OA was employed with machining
parameters of depth of cut, speed and feed. The optimum parameters
were selected, based on the S/N ratios obtained.
Khairi et al. [88] employed Taguchi’s L8 (27) OA to optimize the
end milling parameters with the objective of improving the surface
finish.
Mustafa et al. [89] optimized the turning parameters by
employing parameters that were decided and based on the S/N
calculations and ANOVA results.
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50 Marinkovic Velibor et al. [90] optimized the machining
parameters by employing the Taguchi technique. The work material
used for investigation was cold rolled alloy steel. The objective was to
get good surface finish. Experiments were conducted as per the L27
OA.
2.4 Other Productive and Non-Productive Processes
Kumaruddin et al. [91], in their studies, applied Taguchi’s
Design of Experiments to improve the quality characteristics
(shrinkage) of the plastic tray produced by the injection molding
process. The process parameters were injection speed, temperature,
injection pressure, holding pressure, holding time and cooling time.
The injection speed range was split into two levels and the other
factors were split into three levels. The L18 (21X37) OA with eight
columns and 18 rows was used for this study. ANOVA computations
were used to estimate the contribution of melting temperature
(52.4%), holding pressure (3.6%), holding time (0.9%) and cooling time
(6.9%).
Bastami et al. [92], in their studies, applied the Taguchi
optimization technique to maximize the compressive strength of high
strength concretes. For this work, four control factors were selected.
They were water to cement (W/C) ratio, sand ratio fin (%), Silica fume
and Silica fume ratio (%). Each factor’s range was split into three
levels and 16 experiments were conducted.
Ozturk et al. [93] optimized the seed flow evenness in the seed
drills of the fluted feed rolls. The selected parameters were flute shape,
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51 flute length and rotational speed. Each factor was split into three
levels and experiments were conducted as per the L9 OA.
Rajesh et al. [94] recommended the selection and evaluation of
suppliers based on the quality loss function. The quality loss function
can be nominal-is-best, smaller-is-best or higher-is-best, depending
on the type and on the nature of the component.
Siddesha et al. [95] investigated the effect of the process
parameters of forming process on the microstructure and mechanical
properties of corrugated sheets. The selected process parameters were
strain rate, number of passes and plate thickness. The L27 OA was
opted to study the interactions along with the main effects.
The published work of Adil and Moutawakil [96] is the result of
an experimental investigation carried out to optimize the weight of
glass bottle in manufacturing. The selected manufacturing parameters
were feeder heating temperature, ventilation time and annealing
temperature. Each parameter had three levels. Experiments were
conducted as per the L16 (215) OA and the contribution of parameters
was calculated by ANOVA computations.
The investigation work of Supriyo and Prasanta [97] deals with
the chemical deposition of Ni-P-W coating on mild steel substrate and
optimization of surface roughness. The three coating parameters,
namely concentration of nickel, reducing agent and tungsten, were
varied to obtain optimum conditions using Taguchi method.
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52 Muthu et al. [98] employed the Taguchi loss function to
compute the quality loss per a running unit of time of the injection
molding machine.
The investigation of Reddy et al. [99] deals with the experimental
verification of parameters of a solar parabolic trough in the winter
conditions and to obtain the optimum process parameters by using
the grey relation analysis. The selected parameters were reflector
material, absorber material, position of absorber tube and angle of
absorber. Experiments were conducted as per Taguchi's L9 OA.
Hasani and Hossein [100] studied the effect of different
parameters of siro-jet spun on the performance of the jet. The selected
parameters were main hole diameter, number of sub-holes, inlet and
outlet shapes, angle of sub-holes and sub-hole diameter. Experiments
were conducted as per the L9 OA.
Hosseinpour et al. [101] adopted Taguchi’s L18 OA to optimize
the laser forming. The single objective was the maximization of the
bending angle. The selected parameters were material, power, scan
velocity, beam diameter, sheet thickness, pass number and pulse
duration.
Saravana et al. [102] studied desizing of cotton fabrics using
thermophile -amylases by using Taguchi’s Design of Experiments.
The experiments were carried out with the L9 OA and the process
variables were treatment time, temperature, PH value and enzyme
concentration.
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53 Shiva and Ravi [103] optimized the direct injection single
cylinder diesel engine with respect to brake power, fuel economy and
emissions, through experimental investigations and design of
experiments. The selected parameters were compression ratio,
injection pressure, injection timing, fuel fraction and power. The
experiments were carried out as per the L16 OA.
Pilanee and Jirawate [104] employed the L9 Taguchi design to
enhance the production of white rot fungus. The selected factors were
concentration of cellobiose, peptone, L-asparagine and CuSo4, each
factor having three levels. The result of the study was that the yield
increased with the optimization of the production medium.
Panchakshari et al. [105] applied the Taguchi method to study
the effect of cryogenic parameters on the wear behavior of aluminum-
Al203 metal matrix composites. Experiments were conducted as per
the L9 OA. The selected parameters were temperature, duration of
treatment and reinforcement; and, ANOVA was also applied to
determine the contributions of each factor.
Khan et al. [106] investigated the effect of drilling parameters
namely, spindle speed, drill point angle and feed rate, on the
expansion of hole in acrylic sheet. For this, they selected the L9 OA.
Based on the S/N ratio, the optimum drilling parameters were
determined to give the minimum expansion.
Nikul et al. [107] selected Taguchi’s L27 OA to optimize the
surface finish of the machined components by the peeling operation.
The selected peeling process parameters were cutting speed, feed and
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54 depth of cut. The mean plots showed that among all the parameters,
cutting speed had the maximum impact on surface roughness.
Yogendra et al. [108] optimized the CNC drilling machine
operating parameters with the objective of improving surface finish
and MRR. The selected parameters were spindle speed, feed rate and
depth of cut. Experiments were conducted according to the L9 OA and
the ANOVA computation estimated that the contribution of feed rate
was the highest (39.3%) when compared to other parameters.
Asafa and Fadare [109] optimized the turning parameters to
machine Nigerian Steel 37.2 with uncoated carbide tool, with the
objective of minimizing nose and flank wear. The selected parameters
were cutting speed, feed rate and depth of cut.
Dyi-Cheng Chen et al. [110] applied Taguchi optimization
technique to optimize extrusion parameters. The selected factors were
semi-cone angle diameter of the die hole, friction, velocity of the ram
and temperature of the billet. The L18 OA is employed with objective of
extruding damage free products.
Rashidaee and Asghar [111] employed Taguchi’s Design of
Experiments to optimize the Interior Permanent Magnet motor
dimensions. The results were simulated and confirmed by Finite
Element Analysis.
Karthikeyan et al. [112] optimized the diesel engine parameters
for turpentine mixed diesel using the Taguchi method. The selected
parameters were blending proportion, injection timing and injection
pressure with the objective of improving the brake thermal efficiency.
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55 Prabhu and Vinayagam [113] to employed the Taguchi method
to optimize the electrical discharge machining process. The objective
of the investigation was to obtain a better machined surface. The
machining was performed on carbon nano tubes.
Randhir et al. [114] optimized the process parameters required
to forge A356 aluminum alloy. The selected parameters were forging
temperature, percent reduction and die temperature. Experiments
were conducted as per the L9 (34) OA. The objective was to improve the
hardness.
Shiva et al. [115] used Taguchi’s technique as a methodology to
improve the production of ethanol.
Salehuddin et al. [116] used the Taguchi method to optimize the
effect of halo implant variations on critical values of voltage (Vth) and
leakage current (ILeak) in 45nm P-type metal oxide semiconductor field
effect transistors.
Venkata et al. [117] optimized the carbon dioxide (CO2) molding
parameters. The objective was to achieve better knockout property or
collapsibility. The process parameters selected were Sodium Silicate,
gassing time, mixing time and percentage of coal dust. The L8 OA,
with four factors and two interactions each in two levels, was adopted
for the investigation.
Nikhil and Karunakar [118] optimized the process parameters of
investment casting by employing the Taguchi method, for casting
A713 alloy. The selected parameters were Silica sand mesh numbers,
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56 pre-heating temperature, pouring temperature and pre-heating time.
Experiments were conducted as per the L9 OA.
Senthil et al. [119] applied the L9 OA of the Taguchi technique
to optimize the control parameters of the heat pipe. In this
investigation, the selected control parameters were heat input,
inclination angle and flow rate. Each control parameter range was
split into three levels. Experiments were conducted as per the OA.
ANOVA computations were also done to estimate the contribution of
the control parameters.
Osman et al. [120] applied the L16 OA of the Taguchi technique
to optimize the control parameters of the pavement concrete. In this
investigation, the selected parameters were water to cement ratio,
gradation type, fly ash content and Silica fume content. Each
parameter range was split into four levels. Experiments were
conducted as per the OA. ANOVA computations were also done to
estimate the contribution of each control parameter.
Rangacharyulu and Lal [121] applied the L8 OA of the Taguchi
technique to optimize the process parameters of 500 million transistor
integrated circuit chip. In this investigation, the selected process
parameters were the number of metal layers, minimum feature size,
resistivity, threshold voltage, effective length, saturation drain current
and supply voltage. Each process parameter range was split into two
levels. Experiments were conducted as per the OA.
Maria et al. [122] analyzed the influence of composition and
curing conditions of polyester and epoxy concrete to improve the
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57 bending strength. The Taguchi technique of L16 OA was employed for
this investigation. The selected parameters were Resin type, Resin
content, charge content in Resin, sand type and curing cycle. The
selected factors were analyzed by using the ANOVA technique.
Pascal et al. [123] applied the L16 OA of the Taguchi technique to
optimize the process parameters of water pump of an automotive
combustion engine. In this investigation, the selected process
parameters were primary ring variant, normal load, surrounding,
mating ring finishing and rotational frequency. Each process
parameter range was split into two levels.
Prabir and Reeta [124] applied the L9 OA of the Taguchi
technique to optimize the process parameters of fly ash brick. The
selected process parameters were water/binder ratio, fly ash, coarse
sand and stone dust. Each process parameter range was split into
three levels. Experiments were conducted as per the OA.
Ibhadode and Dagwa [125] applied the L9 OA of the Taguchi technique
to optimize the process parameters of Asbestos friction lining material,
used for automobile disk brake pads. In this investigation, the
selected process parameters were molding pressure, molding
temperature, curing time and heat treatment time. Each process
parameter range was split into three levels. Experiments were
conducted as per the OA.
Ali et al. [126] applied the L16 OA of the Taguchi technique to
optimize the process variables of rotor pole shaped magnet used in DC
motor for industrial applications. In this investigation, the selected
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58 process variables were cogging torque and weighting factors. Each
process variable was split into four levels. Experiments were
conducted as per the OA..
Ismail et al. [127] applied the L9 OA of the Taguchi technique to
optimize the environmental factors of thermal comfort in automotive
parts assembly factory. In this investigation, the selected
environmental factors were luminance (lux), relative humidity (%) and
WBGT (°C). Each process variable range was split into three levels.
Palaniradja et al. [128] applied L9 OA of Taguchi technique to
optimize the process variables in gas carburizing process of SAE 8620
and AISI 3310 Steels used for automobile engineering to improve the
surface hardness. In this investigation the selected process variables
were namely, holding time, carbon potential, carburizing temperature
and quenching time. Each process variable range was split in to three
levels. Experiments were conducted as per OA.
Hsun-Heng Tsai et al. [129] applied the L18 OA of the Taguchi
technique to optimize the dynamic characteristics of a surface
acoustic wave gas sensor.
Yun-Kon et al. [130] applied the L18 OA of the Taguchi technique
to optimize the adhesion strength of Arc Ion Plating of TiAIN Films
used for the hard coating technique of cutting tools in industrial
applications, to increase the hardness of the cutting tools.
Chih Hsiang et al. [131] applied the L18 OA of the Taguchi
technique to optimize the experimental process parameters in the
preparation of Manganese Oxide electrode for the electrochemical
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59 capacitor, to increase the capacitance. In this investigation, the
selected process parameters were concentrations of Manganese
Acetate, temperature, deposition time and deposition voltage.
Gou-Jen et al. [132] applied the L25 OA of the Taguchi
technique to optimize the process parameters of the chemical
mechanical polishing process.
Akshay and Ishtiaque [133] applied L4 OA of Taguchi technique
to optimize the spinning process variables to get optimum the packing
density of textile.
Zaheri et al. [134] applied the L9 OA of the Taguchi technique to
optimize the control parameters of Uranium separation from synthetic
effluents by electrodialysis, in the nuclear industry. In this
investigation, the selected control parameters were feed concentration,
flow rate and voltage. Each control parameter range was split into
three levels. Experiments were conducted as per the OA. ANOVA was
used to determine the factors that influence the separation
percentage.
Cosmin et al. [135] applied the L8 OA of the Taguchi technique
to optimize the process parameters in the Laser Processing of
Polycarbonate Cogwheels.
Eddie et al. [136] applied the L27 OA of the Taguchi technique to
optimize the process parameters of the compressor system to reduce
the length of time needed for its steady state.
Prakash et al. [137] resorted to the L9 OA of the Taguchi
technique to optimize the operating parameters of a four stroke single
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60 cylinder, Karanja oil-operated Kirloskar AV1 D.I. engine. The objective
of the investigation work was to reduce the emission level and
increase the performance.
The literature review concludes that several researchers
conducted experiments on optimization of manufacturing processes,
like casting, machining, powder metallurgy and various welding
methods for different objective functions. However optimization of
SMAW for producing leak-free joints is not found.
SMAW is versatile process and used extensively by the small
and large scale industry, hence enhancement of SMAW process is
both necessary and essential. Presently, industries are finding difficult
to control the parameters of SMAW process, since it is very core type
(not sophisticated) welding process, compare to other high end
welding processes like orbital welding and robot welding process. Due
to this, production of leak - free pipe joints under regular and normal
shop floor condition is very difficult.
Therefore optimization of SMAW process parameters to weld
pipes with the objective of producing leak-free joints has become
synergy of the research work. The gap found in literature survey is
that, economical and versatile SMAW process can be exploited and
explored to meet high quality and quantity requirements of new era
industries, in particular small-scale industries by optimizing the
process parameters. The present work objective is to replace
sophisticated and costly welding process by most economical SMAW.