Vol 2 Issue 6

1. International Journal of Research in Advent Technology, Vol.2, No.6, June 2014
E-ISSN: 2321-9637
Optimization of Machining Parameters of M.S. in Vortex Tube
226
Cooling
Mr. Prabhavalkar M.S.1, Mr. Waghmare S N2
1-2 Assist. Prof., Dept. of Mechanical Engineering, Rajendra Mane College of Engineering and Technology, Ambav (Devrukh),
India.
Email address: prabhavalkar_ms@rediffmail.com(Mr. Prabhavalkar M.S.)
Abstract: Due to the increased demand for engineering products, require efficient machining processes. Increasing environmental
problems prove to be a great hindrance to such processes. Moreover, it is frequently observed that wear of machining parts and
the tool also affects proper machining. Hence this require environment free, cost reduced, user friendly processes which include
reduced tool wear for sustainable machining. Use of vortex tube in machining as a coolant can solve many of these problems.
Optimization methods in turning processes, is considered to play a vital role for continual improvement of output quality in
product and in process parameters relationship and determination of optimal cutting conditions. This study investigated vortex-tube
cooling for machining of mild steel and optimization method of its cutting parameters (feed, cutting speed, dept of cut) to
achieve minimum tool wear. Experimental layout was designed based on L-9 (34) orthogonal array as suggested by Taguchi and
analysis of variance (ANOVA) was performed to identify the effect of the machining parameters on the response variables.
Key words: Vortex tube, Mild Steel, Taguchi, Cutting parameter, Optimization.
1. INTRODUCTION
All Aspects such as tool life and wear, surface finish,
cutting forces, material removal rate, and power consumption,
cutting temperature (on tool and work piece’s surface) decide
the productivity, product quality, overall economy in
manufacturing by machining and quality of machining. During
machining, the consumed power is largely converted into heat
resulting in high cutting temperature near the cutting edge of
the tool. The amount of heat generated varies with the type of
material being machined and machining parameters especially
cutting speed, which has the most influence on the temperature.
Liquid coolants may be toxic or unfriendly to the
environment. Disposal of such harmful coolants is a taboo in
today’s era where pollution is at its peak. Moreover, wear
amongst machining parts and the tool is also a fact to ponder
upon. Tool wear and wear of other parts involved, greatly
harms the efficiency of the process.
In this project we tried to analyze these problems caused
due to tool wear and worked upon it. We made use of vortex
tube as a cooling device. This is our attempt to follow
environment friendly cooling process. Use of atmospheric air
only in cooling solves the problem of disposal. We carried out
turning process on mild steel. Optimization of machining
parameters viz. Feed, cutting speed, depth of cut is carried out
using Taguchi method. Using new combinations of parameters
obtained, experimentation is carried out. Moreover we used
MINITAB software to obtain graphical and accurate results.
2. TAGUCHI METHOD
The Taguchi experimental design method is a well-known,
unique and powerful technique which involves reducing the
variation in a process through robust design of experiments [5]. It is
widely used for analysis of experiment and product or process
optimization. Taguchi has developed a methodology for the
application of factorial design experiments that has taken the
design of experiments from the exclusive world of the
statistician and brought it more fully into the world of
manufacturing. His contributions have also made the
practitioner’s work simpler by advocating the use of fewer
experimental designs, and providing a clearer understanding
of the nature of variation and the economic consequences of
quality engineering in the world of manufacturing. Taguchi
introduces his concepts to:
· Quality should be designed into a product and not
inspected into it.
· Quality is best achieved by minimizing the
deviation from a target.
· The cost of quality should be measured as a
function of deviation from the standard and the
losses should be measured system-wide.
Taguchi recommends a three-stage process to achieve
desirable product quality by design-system design, parameter
design and tolerance design. While system design helps to
identify working levels of the design parameters, parameter
design seeks to determine parameter levels that provide the
best performance of the product or process under study. The
optimum condition is selected so that the influence of
uncontrollable factors causes minimum variation to system
performance. Orthogonal arrays, variance and signal to noise
analysis are the essential tools of parameter design. Tolerance
design is a step to fine-tune the results of parameter design.
.

2. International Journal of Research in Advent Technology, Vol.2, No.6, June 2014
E-ISSN: 2321-9637
227
3. EXPERIMENTATION DETAILS
A. Components used
Cutting tool:
HSS are alloys that gain their properties from either
tungsten or molybdenum, often with a combination of the two.
The tool which we used in the experiment is 3/8”X3”,
9.53X76.20mm, 400S single point cutting tool.
Workpiece material:
The work piece material used in this experiment was MILD
STEEL (MS). Mild steel is often used when large amounts of
steel are needed. Many everyday objects are made up of mild
steel.
Vortex tube:
As stated earlier, we used vortex tube as an apparatus to
enforce efficient cooling at the point of contact during
machining. Use of a vortex tube proves to be highly eco-friendly
as well as user friendly cutting apparatus. It is single
outlet vortex tube and works at a 7 bar.
Fig.1 Setup
4. METHODOLOGY
A. Design of Experiment (DOE)
Design of Experiment (DOE) is a structured and organized
method that is used to determine the relationship between the
different factors of input variables that affects a process and
the output or response of that process. Design of Experiment
involves designing a set of experiments, in which all relevant
factors are varied systematically. When the results of these
experiments are analyzed, they help to identify optimal
conditions, the factors that most influence the results, and
those that do not, as well as details such as the existence of
interactions and synergies between factors. When applied to
product or process design, the technique helps to seek out the
best design among the alternatives.
B. Taguchi’s Robust Design Method
Using Taguchi’s Robust design methodology products can be
produced quickly and at low cost. The idea behind robust
design is to improve the quality of a product by minimizing
the effects of variation without eliminating the causesTo
achieve desirable product quality by design, Taguchi suggests
a three-stage process:
1. System design
2. parameter design
3. Tolerance design.
2.3 Orthogonal arrays
Taguchi has developed a system of tabulated designs (arrays)
that allow for the maximum number of main effects to be
estimated in an unbiased (orthogonal) manner, with a
minimum number of runs in the experiment. Orthogonal
arrays are used to systematically vary and test the different
levels of each of the control factors. Commonly used OAs
include the L4, L9, L12, L18, and L27. In our experiments we
used L9 array. Selecting the number of levels and quantities
properly constitutes the bulk of the effort in planning robust
design experiments.
C. Signal to noise ratio and Pareto ANOVA approach
The S/N ratio developed by Dr. Taguchi is a performance
measure to choose control levels that best cope with noise.
The S/N ratio takes both the mean and the variability into
account. In its simplest form, the S/N ratio is the ratio of the
mean (signal) to the standard deviation (noise). The S/N
equation depends on the criterion for the quality characteristic
to be optimized. While there are many different possible S/N
ratios, three of them are considered standard and are generally
applicable in the situations below;
• Biggest-is-best quality characteristic (strength, yield),
• Smallest-is-best quality characteristic (contamination),
• Nominal-is-best quality characteristic (dimension).
5. EXPERIMENTAL PROCEDURE
A. Selecting the Levels for the Controllable Factors
Levels are selected by conducting screening experiments,
brainstorming session, discussion with production experts,
engineers and taking reference of literature review. The range
is selected between low and high levels of various parameters
(spindle speed, feed rate and depth of cut). These parameters
have been considered as process variables while wear has
been considered as the controllable factor related to work
piece.
FACTORS LEVEL-1 LEVEL-2 LEVEL-3
Spindle speed 90 RPM 190 RPM 375 RPM
Depth Of Cut 1.25 mm 1.5mm 2 mm
Feed 0.094 mm/rev 0.36 mm/rev 0.2 mm/rev
B. Design of Experiments
The aim of the experiments was to analyze the effect of cutting
parameters on the tool wear and workpiece surface temperature
of AISI D2 steel. The experiments were planned using
Taguchi’s orthogonal array in the design of experiments which

3. International Journal of Research in Advent Technology, Vol.2, No.6, June 2014
E-ISSN: 2321-9637
228
help in reducing the number of experiments. The experiments
were conducted according to a three level, L9 (34) orthogonal
array. The cutting parameters identified were cutting speed,
depth of cut and feed. The control parameters and the levels
used in experiment, experimental set up and conditions.
Table A: L9 Taguchi Orthogonal array
C. Machining
Fig. 2 Experimental Setup
The machining tests on the workpiece were conducted
under dry conditions on auto feed lathe which having
maximum spindle speed of 3500 rpm and maximum power of
16 kW.
Before actual turning process, rust removal and leveling of
the work piece was carried out to remove its uneven surface.
Moreover facing was also done. A slight hole was drilled on
the face of the workpiece so that it could be held properly in
the tailstock. Initial dimensions of tool also measured under
Tool maker’s microscope.
D. Experimental Data
After experiments were completed, all the 9 tools were
gathered, and again their dimensions were measured under the
tool maker’s microscope. We obtained the final readings of the
tool dimensions and calculated tool wear comparing with the
readings taken before machining. The readings are as follows:
Table B: Tool wear
E. Analysis of variance (ANOVA)
The experimental results from Table B were analyzed with
analysis of variance (ANOVA), which used for identifying the
factors significantly affecting the performance measures. The
results of the ANOVA with the tool wear are shown in Table
C. This analysis was carried out for significance level of α=0.1
i.e. for a confidence level of 90%.
Table C: ANOVA Results
Sources Of
Variation
Sum
of
Squares
(SS)
De
grees
of
Fre
edom
Mean
Squares
(MS)
F
Ratio
(MS/
Error)
P
Value
%
contrib.
ution
SPEED 0.003267 2 0.001633 0.60
0.624
24.11%
DOC 0.002717 2 0.001358 0.50
0.666
20.05%
FEED 0.002150 2 0.001075 0.40
0.716
15.86%
Residual
Error
0.005417 2 0.002708
Total 0.013550 8
F. Main effect plots
The data was further analyzed to study the interact on
amount cutting parameters (V, D, F) and the main effect plots
on tool wear and workpiece surface temperature were analyzed
Sr.
No.
speed(rpm) feed(mm/rev) depth of
cut(mm)
1 90 0.094 1.5
2 90 0.360 2.0
3 90 0.720 2.5
4 190 0.094 2.0
5 190 0.360 2.5
6 190 0.720 1.5
7 375 0.094 2.5
8 375 0.360 1.5
9 375 0.720 2.0
Initial readings
(mm)
Final readings (mm) Tool wear (mm)
9.340 9.225 0.115
6.345 6.305 0.040
6.345 6.195 0.100
7.495 7.455 0.045
6.315 6.200 0.110
9.200 9.120 0.080
7.370 7.230 0.140
7.940 7.935 0.065
7.405 7.245 0.160

4. International Journal of Research in Advent Technology, Vol.2, No.6, June 2014
E-ISSN: 2321-9637
229
with the help of software package MINITAB15 and shown in
Figures 2 and 3respectively. The plots show the variation of
individual response with the three parameters; cutting speed,
depth of cut and feed separately. In the plots, the x-axis
indicates the value of each process parameters at three level
and y-axis the response value. The main effect plots are used to
determine the optimal design conditions to obtain the low tool
wear.
Fig. 3
When the line is horizontal (parallel to the x-axis), then
there is no main effect present. Each level of the factor affects
the characteristic in the same way and the characteristic
average is the same across all factor levels hence as shown in
above figure 2 there is less effect of feed rate on the variation
in Tool wear.
Fig. 4
The total mean S/N ratio is computed by averaging the total
S/N ratios. Figure 6.1 presents’ main effects plots of the S/N
ratio for the three control parameters speed, feed and depth of
cut studied at three levels for the tool life.
The signal to noise ratio analysis showed that optimized
process parameters corresponding to Tool wear are: Spindle
Speed = 190 rpm, Depth Of Cut = 0.36 mm, Feed Rate =2
mm/rev.
6. CONCLUSION
Vortex-tube cooling has been investigated in dry machining of
mild steel. The following summary can be made:
1. The experimental results showed that the Taguchi
parameter design is an effective way of determining the
optimal cutting parameters for achieving low tool wear.
2. The percentage contributions of depth of cut (24.11%)
and cutting speed (20.05%) in affecting the variation of tool
wear are significantly larger as compared to the contribution of
the feed (15.86%).
3. Significance of machining parameters indicates that
depth of cut is significantly contributing towards machining
performance Therefore; most influencing parameter is depth of
cut for optimizing tool wear.
4. Spindle speed is found to be the most significant
parameter and second significant parameter is Temperature
which has the significant effect on tool wear. Depth of cut is
least significant parameter in all results.
5. From the confirmation tests, good agreement between
the predicted machining performance and the actual machining
performance were observed By taking initial parameter settings
as Spindle Speed = 190 rpm Depth Of Cut = 0.36 mm Feed
Rate = 0.2 mm/rev then tool wear obtained experimentally is
0.041.
.
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