1. International Journal of Research in Advent Technology, Vol.2, No.6, June 2014
E-ISSN: 2321-9637
Process Factor Optimization to Enhance Productivity of
Green Sand Casting Process by Using Taguchi
Methodology
Rajesh Rajkolhe1, J. G. Khan2
Asst Professor1, 2, Mechanical Engineering Department, Shri Sant Gajanan Maharaj College of Engineering,
Shegaon, Maharashtra, India
Email Id: rajeshrajkolhe@gmail.com1 , itsjaweed@yahoo.com2
Abstract- Defects in castings lead to rejection of castings and affect productivity. Blowhole and sand drop are a
kind of defect occurring in castings. Several factors contribute to these defects. Among those, sand particle size,
mould hardness, green compressive strength and permeability are more significant. In the first stage, a set of process
factors that were contributing to these two defects were identified. The identified factors were analyzed using
‘Design of Experiments’ approach. ‘Signal-to-noise’ ratio was estimated. Robust design factor values were
estimated from the ‘signal-to-noise’ calculations. ANOVA analysis was done for robust design factor values. In the
second stage, optimized factor values were adopted in practical runs. It was identified that the optimized values had
improved the acceptance percentage from 91.66% to94.5 %. The improved acceptance percentage had enhanced
productivity of the foundry.
Keywords- Green sand casting, Casting Defect, Taguchi Method, ANOVA
1. INTRODUCTION
A. Taguchi Method
The quality engineering method that Taguchi proposed
is commonly known as Taguchi Method. This is form of
DOE with special application principles. The work of
fractioned is made simple by providing a clear
understanding of the variation nature and economic
consequences of quality engineering in the world of
manufacturing. The philosophy of Taguchi is broadly
applicable and has three stages in process development.
1. System design
2. Parameter Design
3. Tolerance Design
Taguchi recommends that statistical experimental
design methods can be employed to assist in quality
improvements particularly during parameter and
tolerance design.DOE and Taguchi methods have wide
applications in analyzing manufacturing and production
processes. Green sand casting is one of the most widely
used processes to produce parts that cannot be produced
by other manufacturing processes. The
parameters/variables that affect the process are many
and these directly affect the quality of the finished
casting.
This paper summarizes the following:
i) Improving quality of green sand castings
through process control, keeping the effects of
uncontrolled parameters at a minimum level.
ii) Analyze and select the most significant
parameters that affect quality characteristics.
iii) Select an appropriate orthogonal array and
suitable levels of parameters. Collect related
experimental data.
iv) Analyze the data using DOE software and
generate ANOVA table, interaction graphs
response graphs.
v) Decide on the optimal settings for the control
parameters.
vi) Validate the optimum setting levels in reducing
the levels of the Quality Characteristics (Casting
defects)
B. Process parameters of Green sand casting
The following process parameters are identified as
significant and their levels are listed in Table 1:
2. International Journal of Research in Advent Technology, Vol.2, No.6, June 2014
E-ISSN: 2321-9637
Factor name Designation Level 1 Level Level 3
Sand particle size(AFS) A 40 45 50
Mould hardness(NU) B 60 70 80
Green compressive
Strength(gm/cm^2)
C 1000 1100 1200
Permeability(NU) D 115 135 145
Table1. Process parameters and their levels (range)
2. EXPERIMENTAL PLANNING
The first step in Taguchi method is to select an
appropriate OA (orthogonal array). The choice of a
suitable OA design is critical for the success of
experimental design and this depends upon degrees
of freedom required to study main effect and
interaction effects, resource availability and time
constraints. Amongst the standard OA’s L4, L9, L16,
L18 etc, L9 was found most appropriate to study 4
factors at three levels. The OA selected, process
parameters and interaction assigned are given in table
2.
Trial
No.
A B C D
1) 1 1 1 1
2) 1 2 2 2
3) 1 3 3 3
4) 2 1 2 3
5) 2 2 3 1
6) 2 3 1 2
7) 3 1 3 2
8) 3 2 1 3
9) 3 3 2 2
Table2. Orthogonal array L8 (Control factors
assigned)
The experimental OA will look like as follows:
Trial
A B C D
No.
1) 40 60 1000 115
2) 40 70 1100 135
3) 40 80 1200 145
4) 45 60 1100 145
5) 45 70 1200 115
6) 45 80 1000 135
7) 50 60 1200 145
8) 50 70 1000 145
9) 50 80 1100 135
Table3. Experimental Orthogonal Array
3. EXPERIMENTATION
The experiments are conducted against the trail
condition tabulated in table 4. The defects resulting
from molding processes only are identified, and the
percentage approved castings was calculated and
recorded in table 4.The quality characteristics is
Approved percentage of casting and so “Larger is
better” analysis is performed.
Trial
No.
A B C D %
Approved
casting
1) 40 60 1000 115 80.48
2) 40 70 1100 135 83.72
3) 40 80 1200 145 89.60
4) 45 60 1100 145 92.55
5) 45 70 1200 115 86.88
6) 45 80 1000 135 95.00
7) 50 60 1200 145 95.30
8) 50 70 1000 145 98.50
9) 50 80 1100 135 95.50
Table4. Approved percentage of casting
3.1. Signal-to-noise ratio evaluation:
As an evaluation tool for determining the robustness
of the design, ‘signal-to-noise’ ratio (SNR) is the
most important component of the factor design. In the
Taguchi method, the term ‘signal’ represents the
desirable target (higher percentage of approved
castings) and ‘noise’ represents the undesirable value.
The SNR for each factor level is calculated using the
following formula.
Where- ‘n’ is the number of experiments Conducted
at level ‘i’ and ‘yi’ is the approved percentage (A %)
of parameter ‘y’. A robust system will have a high
SNR. SNR should be as large as possible for higher
3. International Journal of Research in Advent Technology, Vol.2, No.6, June 2014
E-ISSN: 2321-9637
values of approved percentages. Table 4
average SNR for each at the Signal level and factors,
respectively. Table 5 shows the r
optimum value of factors for maximization of
approved castings.
LEVEL A B C
robust design
1 38.54 39.01 39.18
2 39.22 39.03 39.13
3 39.68 39.40 39.14
shows the
D
38.83
39.20
39.41
Table 5. Average SNR values for each signal values and factors
FACTOR SNR LEVEL
OPTIMUM
VALUE
50
80
1000
145
A 39.68 3
B 39.40 3
C 39.18 1
D 39.31 3
Table 6. Robust design optimum value of factors for maximization
of approved castings.
3.2. ANOVA analysis
Analysis of variance (ANOVA) is an
analytical
method to square the dispersion of specific numbers.
The factor that has much influence on response
variable is identified through the percentage of
contribution. The factor, which has more percentage
of contribution, is the significant factor.
ANOVA is
widely used for determination of percentage
contribution. The
Procedural steps of ANOVA are outlined below.
1) The first step is to calculate the sum of
square for each of the factor and the total
(SS).
2) Set degrees of freedom (DOF) for each
parameter.
DOF = number of levels of parameter ‘i’
TOTAL D.O.F = N
D.O.F of error = TOTAL D.O.F
factor
3) Calculation of mean square for each factor
(MS).
4) Calculation of F column or mean ratio.
5) Selection of F tabulated valve for
6) Comparison of F
7) Identification of Significant parameters.
(F statistic ≥
(If F statistic ≥ F tabulated then that particular
parameter is most significant).
8) Calculation of % contribution (P).
Factor D.F SS
A 2 211.88
B 2 28.90
C 2 1.08
D 2 53.88
Error 2 53.88
– 1
N-1
- ΣDOF of
Or
v1, v2.
statistic and F tabulated
F tabulated)
culation MS F P
105.94 7.51 71.64
14.45 26.98 9.77
0.540 26.98 0.33
26.40 3.60 -
26.40 - 18.22
4. International Journal of Research in Advent Technology, Vol.2, No.6, June 2014
E-ISSN: 2321-9637
Total 8 295.75 99.96
Table 7. Percentage contribution of factors for
percentage approved castings.
3.3 PERCENTAGE OF CONTRIBUTION OF
FACTORS
4. Performance evaluation:-
The final step in the process factor design is to
validate the ANOVA results with selected optimum
process factors.
· Factor A- Sand particle size is maintained at
50 (AFS),
Factor B- Mould hardness-is maintained at
80(nu)
Factor C- GCS(g/cm^2) is maintained at
1000(g/cm^2)
Factor D- Permeability is maintained at
145(nu)
This robust design setting (optimum process
parameter) implemented in the month of December.
Data was recorded with the robust design settings, as
mentioned above and the results are the number of
castings poured, the number of castings approved and
their respective rejection percentages are tabulated
below.
Trial
No
No. Of
Casting
No. Of
Casting
% Of
Casting
1 39 3 92.30
2 35 2 94.28
3 35 2 94.28
4 35 1 97.14
5 35 1 97.14
6 37 3 91.89
7 37 3 91.89
8 37 2 94.59
9 37 1 97.29
Table8:- Approved percentage of casting after employing robust
design factors value.
Factors Existing
range
Optimal
range
A 45 50
B 70 80
C 1000 1000
D 135 145
%
Approved
castings
91.66% 94.53%
Table9:- Existing and estimated optimal factor Level
5. CONCLUSION
The control factors sand particle size, mould
hardness have significant effect on the
process, percentage defects as the evidenced
by the percentage contribution.
The optimized levels of selected process
parameters obtained by Taguchi method are:
Factor A–Sand particle size is maintained at
50 (AFS),
Factor B - Mould hardness-is maintained at
80(nu)
Factor C- GCS is maintained at 1000
(g/cm^2)
Factor D- Permeability is maintained at
145(nu)
With Taguchi optimization method the %
acceptance of castings has increased from
91.66% to 94.5%.
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