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International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
All rights are reserved 1
SOME STUDY OF MACHINABILITY BY TURNING PROCESS-A
REVIEW
* Saadat Ali Rizvi and
* * Wajahat Ali and* * * Mumtaz Ahmad
* Research Scholar, Department of Mechanical Engineering, IIT (BHU), Varanasi (U.P) and
faculty member in U.P, J.M.I,New Delhi, INDIA
**Mechanical Engineering Department, SCRIET, (CCS University, Meerut) , INDIA
***Faculty member in U.P, J.M.I,New Delhi , INDIA
Abstract
In this review paper an attempt has to been made to study of different parameters which
affect the mahinability of different materials which are commonly used to produce
automotive parts such as shafts, gears and bearings. Surface quality is one of the most
impellent customer requirements in machining of soft and hard turning. The main aspect of
surface quality on machined parts is probably surface integrity, such as roughness and
residual stresses. There are three controllable factors in turning process viz. cutting speed,
feed and depth of cut. It is seen that the insert radius, feed rate, and depth of cut have
different effects on the surface roughness. In this research paper various optimization
technique such as Taguchi, RSM, and ANN etc method as well as insert are reviewed.
Taguchi method a powerful tool for experiment design is also used to optimize the cutting
parameters to achieve better surface finish and to identify the most effective parameter for
cost evolution during turning.
Keywords: CNC turning, Taguchi method, ANOVA, Machining, Machinability.
1. Introduction:
In modern industry the basic goal of manufacturer is to manufacture the product at low cost,
high quality products in short time. For this purpose Automated and flexible manufacturing
systems are employed with computerized numerical control (CNC) machines that are capable
of achieving high degree accuracy and low processing time. Among the entire machining
process turning is the first most common method for cutting and especially for the finishing
machined parts. In a turning operation, it is important task to select cutting parameters for
achieving high cutting performance.
Cutting parameters affect the surface roughness, surface texture and dimensional deviations
of the product. Surface roughness, which is used to determine and to evaluate the quality of a
product, is one of the major quality attributes of a turning product. Three cutting parameters
namely, insert radius, feed rate, and depth of cut, are optimized with considerations of surface
roughness. The mechanism behind the formation of surface roughness is very complicated
and process dependent [1]
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
All rights are reserved 2
Fig. 1. Surface roughness profile [4]
N. Satheesh Kumar et al [2] investigates the effect of process parameters in turning of
Carbon Alloy Steels in a CNC lathe. The parameters namely the spindle speed and feed rate
are varied to study their effect on surface roughness. The experiments are conducted using
one factor at a time approach. The five different carbon alloy steels used for turning are
SAE8620, EN8, EN19, EN24 and EN47. The study reveals that the surface roughness is
directly influenced by the spindle speed and feed rate and at the last they observed that the
surface roughness increases with increased feed rate and is higher at lower speeds and vice
versa for all feed rates.
Surface Roughness Profile of EN8 at 339 rpm and 0.05 mm/rev
Surface Roughness Profile of EN19 at 730 rpm and 0.125 mm/rev
Surface Roughness Profile of EN24 at 430 rpm and 0.125 mm/rev
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
All rights are reserved 3
Surface Roughness Profile of EN47 at 980 rpm and 0.15 mm/rev
Fig 2 Surface roughness graph of different material at different speed
W.S. Lin et al [3] develop a model for surface roughness and cutting force This network is
composed of a number of functional nodes, which are self-configured to form an optimal
network hierarchy by using a predicted square error (PSE) criterion. Once the process
parameters (cutting speed, feed rate and depth of cut) are given, the surface roughness and
cutting force can be predicted by this network. To verify the accuracy of the abdicative
network, regression analysis has been adopted in the paper to develop a second prediction
model for surface roughness and cutting force. Comparison of the two models indicates that
the prediction model developed by the abdicative network is more accurate than that by
regression analysis. Experimental results are provided to confirm the effectiveness of this
approach and they conclude that Crucial factors that control the cutting force are the feed rate
and the depth of cut, where the cutting force tends to increase with an increased feed rate and
depth of cut
The models for surface roughness Ra and cutting force F are derived from the data shown in
Table 1 and as defined below:
Ra =-2.172026+ 0.035321V – 0.000103V2+ 86.164152f
2 – 0.037214Vf + 3.856817fd
F=-161.988683+ 2.351115V+ 429.783951f+ 423.407407d- 0.009119V2 -
47.407407d2+1692.283951fd
Fig 3.Adductive network for surface roughness
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
All rights are reserved 4
Fig 4.Adductive network for cutting forces
Uma Maheshwera Reddy Paturi et al [4] studied the effect of solid lubricant assisted
minimum quantity lubrication (MQL) turning of Inconel 718 on the finish quality of
machined surface by turning process. In MQL application, micron sized tungsten disulfide
(WS2) solid lubricant powder particles were dispersed (0.5% wt.) in emulsifier oil based
cutting fluid (20:1). To comprehend the finish quality of machined surface with the presence
of WS2 solid lubricant, turning tests are conducted. The effect of cutting parameters on the
finish quality of work surface is evaluated using statistical design approach. Multiple linear
regression models are developed and validated to understand the correlations between cutting
parameter .and they find out that surface quality of machined work material during WS2 solid
lubricant assisted MQL machining showed a much improvement on the finish quality of work
material by on average about 35% when compared to MQL machining alone.
Gabriel Medrado Assis Acayaba , Patricia Mun˜oz de Escalona[5] developed a prediction
models to predicting the surface roughness by using Multiple Linear Regression and
Artificial Neural Network methodologies. Results show that the neural network outperforms
the linear model by a fair margin (1400%). Moreover, the developed Artificial Neural
Network model has been integrated with an optimisation algorithm, known as Simulated
Annealing (SA), this is done in order to obtain a set of cutting parameters that result in low
surface roughness. A low value of surface roughness and the set of parameters resulting on it,
are successfully yielded by the SA algorithm and finally they conclude that the cutting
conditions obtained by the SA algorithm for low values of surface roughness are V = 49.3
m/min, f = 0.156 mm/ rev, d = 0.52 mm and r = 0.57 mm.
Fig. 5. Schematic drawings of turning insert geometry
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
All rights are reserved 5
The proposed regression equation with potential adjustment is given by
Ra = 101:31
V-0:424
f 0:734
d0:187
r-0:499
R2 =0:466,R
2adj = 0:460 and σ = 1:33
Anupam Agrawal et al [6]studied the effect of cutting parameters in influencing the
machined surface roughness The machining outcome was used as an input to develop various
regression models to predict the average machined surface roughness on this material. Three
regression models – Multiple regression, Random forest, and Quantile regression were
applied to the experimental outcomes The performance of these models was compared to
ascertain how feed, depth of cut, and spindle speed affect surface roughness and finally to
obtain a mathematical equation correlating these variables and they concluded that the
random forest regression model is a superior choice over multiple regression models for
prediction of surface roughness during machining of AISI 4340 steel (69 HRC)
Table no 1[6]
2. Taguchi method
Mustafa Gunay & Emre Yucel [7] optimizing the cutting conditions for the average surface
roughness (Ra) obtained in machining of high-alloy white cast iron (Ni-Hard) at two different
hardness levels (50 HRC and 62 HRC). Machining experiments were performed at the CNC
lathe using ceramic and cubic boron nitride (CBN) cutting tools on Ni-Hard materials.
Cutting speed, feed rate and depth of cut were chosen as the cutting parameters. Taguchi L18
orthogonal array was used to design of experiment. Optimal cutting conditions was
determined using the signal-to-noise (S/N) ratio which was calculated for Ra according to the
„„the-smaller-the-better‟‟ approach. The effects of the cutting parameters and tool materials
on surface roughness were evaluated by the analysis of variance and they finally conclude
that the smallest Ra values occurred during machining of Ni-Hard with 62 HRC and Ni-Hard
with 50 HRC are obtained as 0.262 lm and 0.280 lm with CBN cutting tool, respectively.
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
All rights are reserved 6
M. Nalbant et al [8] study, the Taguchi method is used to find the optimal cutting parameters
for surface roughness in turning. The orthogonal array, the signal-to-noise ratio, and analysis
of variance are employed to study the performance characteristics in turning operations of
AISI 1030 steel bars using TiN coated tools. Three cutting parameters namely, insert radius,
feed rate, and depth of cut, are optimized with considerations of surface roughness.
Experimental results are provided to illustrate the effectiveness of this approach and they told
that in turning, use of greater insert radius (1.2 mm), low feed rate (0.15 mm/rev) and low
depth of cut (0.5 mm) are recommended to obtain better surface roughness for the specific
test range.
Total mean of the S/N ratio g can be calculated as
Where m is the number of experiments in the orthogonal array, .g., m = 9 and gi is the mean
S/N ratio for the ith
experiment
Squared deviations SST can be calculated as
where p represent one of the experiment parameters, j the level number of this parameter p, t
the repetition of each level of the parameter p, sgj the sum of the S/N ratio involving this
parameter p and level j.
The sum of squares from error parameters SSe is
The corrected sum of squares SP can be calculated as
The estimated S/N ratio ƞ using the optimal level of the process parameters can be calculated
as
Bouacha et al [9] Investigated the Ra values created in hard turning of 64 HRC hardness
AISI 52100 bearing steel with CBN cutting tool. They performed the machining tests
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
All rights are reserved 7
according to the L27 orthogonal array of Taguchi experimental design method. They reported
that the cutting parameter which is the most effective on Ra is the feed rate and cutting speed.
Asilturk and Akkus[10] uses the Taguchi method for minimizing the average Surface
roughness (Ra) and the arithmetic mean value of the single roughness depths of consecutive
sampling lengths (Rz) in turning of hardened AISI 4140 (51 HRC) with coated carbide
cutting tools. Their study focused on effects of cutting speed, feed rate and depth of cut on
surface roughness. Statistical analysis of experimental data indicated that the feed rate was
the most significant effect on Ra and Rz. The authors were also found that the optimum
cutting conditions was different for Ra and Rz.
Mangesh R. Phate and V.H.Tatwawadi [11] developed a model to calculate the Material
Removal Rate (MRR) & Power Consumption (PC) using Dimensional Analysis (DA). DA
models of MRR & PC have been developed with machining field parameters. The
parameters were the operator performing the turning operation, the cutting tool used to
remove the material, the work piece, the cutting process parameters, such as cutting speed,
feed rate, depth of cut etc., and lathe machining specifications and the turning
environmental parameters, such as humidity, atmospheric temperature, air circulation,
noise level, and light illumination etc and finally they told that the cutting condition and the
lathe machine parameters have significant effects on the material removal rate and the
power consumption, while the tool and the environment have the least effect.
Model Formulation
Model: Material Removal Rate (MRR) Model
𝜋D1= 0.00026743 × 𝜋10.1408× 𝜋2 −0.0293 × 𝜋3
0.324 × 𝜋40.520 × 𝜋5
−0.0415 × 𝜋60.4412
• Correlation Coefficient = 0.982915516
• Root Mean Square Errors=0.034305626
• Reliability = 98.25331313%
It is obvious that the predicted values by DA are very close to the experimental readings.
Model: Power Consumption (PC) Model
𝜋D2= 9.65 𝐸−05× 𝜋1−0.0545× 𝜋2 −0.0495 × 𝜋3
0.5267 × 𝜋4−0.1369 × 𝜋5
0.1072 × 𝜋6−0.1983
• Correlation Coefficient = 0.98203603
• Root Mean Square =0.03476825
• Reliability = 98.1258013%
Z= Log (Π D1), K= Log (K1), X1= Log (Π1), X2 = Log (Π2) X3= Log (Π3), X4= Log (Π4), X5= Log (Π5
and X6=Log (Π6)
Z1 (Max MRR) = -8.228 +0.1408X1 -0.0293 X2 +0.324X3 +0.5200 X4 - 0.0415X5 +0.4412X6
Z2 (Min PC) = -9.2455 -0.0545X1 -0.0495 X2 +0.5267X3 -0.1369 X4 +0.1072 X5 -0.1983 X6
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
All rights are reserved 8
Fig 6.Flow Chart for regression Analysis
M. Gunay, A. Kacal, Y. Turgut [12] studied about the Taguchi method, which is used in the
industry to decrease the product development period for the design and production which also
decrease the costs and increase the profit of the company. Taguchi method also allows
controlling the variations caused by the uncontrollable factors which are not taken into
consideration at conventional design of experiment. Taguchi converts the objective function
values to signal-to-noise (S/N) ratio for measure the performance characteristics of the levels
of control factors against these factors. S/N ratio is defined as the desired signal ratio for the
undesired random noise value and shows the quality characteristics of the experimental data.
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
All rights are reserved 9
S. A. Hussain et al [13] discusses the use of fuzzy logic for modeling turning parameters in
turning of glass fiber reinforced plastics by carbide tool (K-20). Experiments were conducted
based on the established Taguchi‟s Design of Experiments (DOE) L25 orthogonal array on an
all geared lathe. The cutting parameters considered were cutting speed, feed, depth of cut, and
work piece (fiber orientation). Fuzzy based model is developed for correlating the cutting
parameters with surface roughness (Ra). And finally they find that the model can be
effectively used for predicting the surface roughness (Ra) in turning of GFRP composites.
Kopac et al. [24] considered cutting speed, cutting tool materials, feed rate and depth of cut
as cutting parameters in machining C15 E4 steel on a lathe. They used the Taguchi
orthogonal array of L16 (25), which has two levels and a degree of freedom of 13 in the
experimental design. The quality determinant of „„the smaller the better‟‟ was used in
calculating the signal–noise ratio. It was observed that the control parameter having the
highest effect on surface roughness is the cutting speed, and better surface roughness values
were obtained at higher cutting speeds.
3. Response Surface Methodology (RSM)
Satish Chinchainkar et al [14] investigate effect of different cooling medium and cutting
parameters on surface roughness during the turning of AISI 52100 steel. They generate a
mathematical model. experiment conduct by using a PVD-coated TiSiN-TiAlN carbide tool
under dry, with water based and coconut oil-based cutting fluids and they find out that hard
turning under dry conduction produced lower values of surface roughness.hower at higher
cutting speed hard turning using coconut oil produced lower value of surface roughness. It is
also observed that surface roughness mostly affected by feed and increased when cutting
speed exceeds 150-160 m/min irrespective of the cooling medium used. Stat-Ease Design
Expert software use to calculate the regression equation. Analysis of the experimental result
was performed using standard Response Surface Methodology (RSM).
Surface roughness: Dry condition
Surface roughness: Water based cutting fluid
Surface roughness: Coconut oil based cutting fluid
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
All rights are reserved 10
Fig 7. Response surface showing interactions effects of cutting speed and feed on surface
roughness for (a) Dry ;(b) water based;(c)Coconut oil based cutting fluids
Varaprasad Bhemuni and Varaprasad Bhemuni [15] studied the effects of cutting speed;
feed rate and depth of cut on surface roughness are investigated in hard turning of AISI D3
steel. AISI D3 steel is hardened to 62 HRC and is machined using a mixed ceramic tool.
Mathematical models for surface roughness are developed using the Response Surface
Methodology (RSM). Central Composite Design (CCD) is applied as an experimental design.
Al2O
3/TiC mixed ceramic tool with corner radius of 0.8 mm is employed to accomplish 20
tests with six centre points. The range of each parameter is set at three different levels,
namely low, medium and high. The main effects of the factors and their interactions were
considered in the present study using Analysis of variance (ANOVA). Various graphs and
plots are drawn to evaluate the influence of the process parameters on surface roughness.
Feed rate is the most influencing factor on surface roughness. Results revealed that the use of
lower feed, lower depth of cut and higher cutting speed, while hard turning of AISI D3
hardened steel, ensures a better surface roughness.
In the present study, the quantitative form of the relationship between the desired response
and independent input process parameters can be represented by [9]
Y=Φ (Vc, f, ap) 𝑌=𝑎0+𝑎1𝑉𝑐+𝑎2𝑓+𝑎3𝑎𝑝+𝑎12𝑉𝑐𝑓+𝑎13𝑉𝑐𝑎𝑝 +𝑎23𝑓𝑎𝑝+𝑎11𝑉
2𝑐+𝑎22𝑓2+𝑎33𝑎
2𝑝
Where Y is the desired response and is the response function. In the present investigation,
the RSM-based mathematical models for surface roughness Ra, have been φ developed with
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
All rights are reserved 11
cutting speed Vc, feed rate f and depth of cut (Doc) as the process parameters. The response
surface equation for three factors is given by [16]
Where Y is the desired response and a0
is the free term of the regression equation, the
coefficients a1, a
2, a
3 and a
11, a
22, ...a
33 are the linear and quadratic terms, respectively, while
a12
, a13
,..a23
are the interacting terms. The experimental plan is developed to assess the
influence of cutting speed (Vc), feed rate (f), and depth of cut (Doc) on the surface roughness
(Ra), regression coefficients to be determined for each response. The regression coefficients
of linear, quadratic, and interaction terms of RSM-based mathematical models are determined
by [16]
Fig 8. Workpiece with carbide insert CC6050[16]
Murat Sarıkaya and Abdulkadir Güllü[17] studied the effect of the main turning
parameters such as cooling condition, cutting speed, feed rate and depth of cut on arithmetic
average roughness (Ra) and average maximum height of the profile (Rz) when turning of AISI
1050 steel. Experiments have been performed under dry cutting (DC), conventional wet
cooling (CC) and MQL. Tests are designed according to Taguchi‟s L16 (43X 2
1) orthogonal
array. ANOVA analysis was performed to determine the importance of machining parameters
on the Ra and Rz and they conclude that the most effective parameters are feed rate on the
surface roughness. Cooling conditions are significantly effective on the surface roughness.
MQL is a good tool in order to increase of the machined surface quality for cutting operations
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
All rights are reserved 12
Fig 9. A schematic diagram of the experimental set-up.[17]
RSM based modeling for surface roughness: In this paper, MINITAB 16 and DESIGN
EXPERT 8.0 were used for modeling purpose: a second order model response surface can be
fitted into the following Eq
Surface roughness and turning parameters are given below
Relationship between surface roughness and machining variables
RSM surface roughness Ra model is given by
Shetty et al. [18] used Taguchi and Response Surface Methodologies for minimizing the
surface roughness in turning of discontinuously reinforced aluminum composites (DRACs)
having aluminum alloy 6061 as the matrix and containing 15 vol.% of silicon carbide
particles of mean diameter 25 lm under pressured steam jet approach. The effect of cutting
parameters on surface roughness was
Evaluated and the optimum cutting condition for minimizing the surface roughness was also
determined
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
All rights are reserved 13
4. ANN Technique
Ilhan Asiltürk and Mehmet Çunkas[19] measure the surface roughness during turning at
different cutting parameters such as speed, feed, and depth of cut. Full factorial experimental
design is implemented to increase the confidence limit and reliability of the experimental
data. Artificial neural networks (ANN) and multiple regression approaches are used to model
the surface roughness of AISI 1040 steel. Multiple regression and neural network-based
models are compared using statistical methodsand they proposed models which are capable
of prediction of the surface roughness. The ANN model estimates the surface roughness with
high accuracy compared to the multiple regression model
Fig 10.Mathematical model of neuron[19]
Fig.11 ANN structure[19]
Konanki M. Naidu[20] developed a model of surface roughness in turning of AA 6351
alloy with carbide tool. Cutting speed, feed and depth of cut were considered as machining
parameters and surface roughness was considered as the response. Experiments were
conducted to develop the linear regression equations based on Taguchi‟s experimental design
methodology. Moreover, Artificial Neural Network (ANN) model was also developed for the
surface roughness. Further, the performance of the developed model has been tested with the
help of ten experimental test cases and they concluded that the multiple linear regression
analysis the interaction terms of speed, feed and depth of cut are not significant on the
response surface roughness.
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
All rights are reserved 14
Fig 12.Structure of feed-forward neural network [20]
B. Anuja Beatrice et al [21] predict the Surface Roughness of AISI H13 Steel by using
Artificial Neural Network in Hard Turning with Minimal Cutting Fluid Application. In this
research work an attempt was made to develop a model based on Artificial Neural Network
to simulate hard turning .This model is expected to predict the surface roughness in terms of
cutting parameters. Networks with different architecture were trained using a set of training
data for a fixed number of cycles and were tested using a set of input / output data reserved
for this purpose. The root mean square error was determined for the selected architectures.
The model with 3-7-7-1 architecture gave the minimum RMSE value. The ability of ANN
model to predict surface roughness (Ra) was analyzed. It was found that the predictions made
by the ANN model matched well with the experimental results.In this present work,
„learngdm‟ was considered as the learning function and „trainlm‟ as the training function. The
transfer function of the ANN model was considered as “tansig” and the sigmoid function
used in this experimentation is shown in equation f(x)
5. Analysis of Variance (ANOVA)
A.M.Badadhe et al [22] studied Four parameters viz. spindle speed, feed, depth of cut and
length to diameter (L/D) ratio of boring bar has been taken as control factors for optimizing
cutting parameters. The cutting trials were performed as per Taguchi 34 (L9 ) orthogonal
array method to deal with the response from multi-variables. AISI 1041 (EN9) carbon steel
was used as a job material which was cut by using standard boring bars of various sizes each
having a tungsten carbide inserts of same insert radius. The Analysis of Variance (ANOVA)
was carried out to find the significant factors and their individual contribution in the response
function i.e. surface roughness and they conclude that optimal turning operation parameters
for surface roughness under varying conditions through the use of the Taguchi parameter
design process and they showed that the control factors had varying effects on the response
variable. The use of the Taguchi parameter design technique was considered successful as an
efficient method to optimize machining parameters
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
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6. Inserts or Cutting Tools
D.M. D'Addona and Sunil J Raykar[23] investigate performance of wiper inserts in hard
turning of oil hardening non-shrinking steel. The oil hardening non-shrinking steel is
commonly used material for making measuring instruments and gauges wherein surface
roughness is very important aspect. The major emphasis here is given to study and compare
performance of wiper insert in terms of surface finish with conventional inserts. Influence of
process parameters such as speed, feed, depth of cut and nose radius (for wiper and
conventional inserts) on surface roughness is analyzed using analysis of variance (ANOVA)
and analysis of means (AOM) plots. And finally they result that wiper inserts produce a very
good machined surface compared to conventional inserts.
K. Palanikumar et al [24] studied the factors which affect the cutting parameters of surface
roughness such as Ra, Rt, Rq, Rp and R3z in turning of glass fiber reinforced composite
materials. Empirical models are developed to correlate the machining parameters with
surface roughness .Empirical models are developed to correlate the machining parameters
with surface roughness. Analysis of experimental results is carried out through area graphs
and three-dimensional surface plot and finally the concluded that surface roughness
increases with the increase of feed rate and almost decreases with the increase of cutting
speed.
Fig13. Polycrystalline diamond tool and tool holder used in experiment[24]
Junyun Chen, Qingliang Zhao [25] tries to improve a method to evaluate the actual relative
tool-work vibration. By using this method the vibration information obtained is more
credible, as it contains the components caused by machine tool error, cutting force, material
property and changing of cutting parameters. Moreover, the swelling effect is analyzed using
a new evaluating method and taken into account for predicting surface roughness and they
prove that this model is a closer approximation of the actual turning process as compared to
the previous models and shows a higher predicting accuracy of surface roughness.
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
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Fig 14. CNC ultra-precision machine tool (left) and diamond tools used in tests (right)[25]
The arithmetic roughness value Rat calculated from the profile of tool locus and the arithmetic
roughness value Rah calculated from the curve of simple harmonic motion can be given
respectively.
&
Then equivalent amplitude A can be calculated by
Fig 15.Three-dimensional topography and surface profile of machined surface. (a) Material:
NiP; (b) material: Cu; (c) material: Al7075[25]
A.Srithar et al [26] carried out the machining of AISI D2 steel workpiece having 66 HRC
hardness using coated carbide insert. The microstructure shows rolled grains of the steel
along the direction of the material. The microstructure shows fine grains of cementite with
the grain boundary chromium and other alloys and the presence of carbide, which increases
strength and wear resistant. Investigations were carried out on conventional lathe using the
prefixed cutting conditions. The graph shows the feed rate is the main impact with increasing
feed rate, but reduces with larger cutting speed and rapidly increasing depth of cut. The
responses studied in the investigation of surface roughness are studied in the investigation of
surface roughness parameters (Ra,Rt and Rz) on responses are studied and presented in detail.
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
All rights are reserved 17
Fig.16. Surface roughness measurement by roughness tester[26]
Hamza Bensouilah et al [27] study of the effects of cutting speed, feed rate and depth of cut
on the performance of machining which traditionally named „„machinability with CC6050
and CC650 ceramic inserts. The planning of experiments was based on Taguchi‟s L16
orthogonal array. The analysis of variance (ANOVA), the signal-to-noise ratio and response
surface methodology (RSM) were adopted and finally they find that the surface quality
obtained with the coated CC6050 ceramic insert is 1.6 times better than the one obtained with
uncoated CC650 ceramic insert. However, the uncoated ceramic insert was useful in reducing
the machining force.
RMS-based first order mathematical model is given by
where b0 is the free term of the regression equation, the coefficients, b1; b2; . . . ; bk are the
linear terms. The experimental plan is developed to assess the influence of cutting speed (Vc),
feed rate (f) and depth of cut (ap) on the surface roughness parameter (Ra) and cutting force
component
Taguchi method: For smaller-the-better, the S/N ratio is defined as
For nominal-the-best, the S/N ratio is defined as:
For larger-the-better, the S/N ratio is defined as
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
All rights are reserved 18
Fig 17. Illustration of measured force components, surface roughness and tool wear [27].
7. Conclusions
A From the above literature survey we find that there are some latest techniques for
optimization, Taguchi technique, Fuzzy Logic, Scatter Search technique, ANN, Genetic
Algorithm, Response Surface Methodology, Ant Colony technique, etc. are being applied
successfully in industrial applications for optimal selection of process variables for evaluation
the surface roughness of different materials. Among all these methods it is observed that
Taguchi Method is the most widely used method due to its simplicity and accuracy. The use
of other methods like Genetic Algorithm, Response Surface Method and Artificial Neural
Network are gradually increasing. In optimization of surface roughness feed is found to be
the most affecting factor followed by depth of cut and cutting speed
International Journal of Interdisciplinary Research Centre (IJIRC) ISSN: 2455-2275(E)
Volume II, Issue 6 June 2016
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