Download Report

QUALITY IMPROVEMENT OF ALUMINIUM
ALLOY (LM-6) CASTING USING TAGUCHI
METHOD
Ravneet kakria ∗ , Chandandeep Singh**, Priyavrat Thareja***
ABSTRACT
There is no alternative to the intricacy; evidenced through sand casting as
a production route, economical and poised for shorter runs. Quality of
castings and parametric control thereof is more important than that of die
casting. With increasing demand for high-quality castings @ close
tolerances, a need however was felt to study ways to get the optimal
settings for main parameters to ensure better surface finish. The
requirement in unison was hitherto attempted in case of LM-6 Aluminum
alloys castings, developed through green sand casting route. Five main
parameters namely Bentonite clay, AFS Grain Fineness Number,
Moisture, Pouring temperature and Coal dust were duly identified. The
effects of the selected process parameters on the surface finish and the
subsequent optimal settings of the parameters were accomplished using
Taguchi’s method. Using L8 (27) orthogonal arrays, experiments were
conducted as per experimental plans given in this array. The results
indicate that the selected parameters significantly affect the surface finish
of LM-6 Aluminum alloys castings. The confirmatory experiments have
also been carried out to verify the optimal settings of the parameters.
KEY WORDS: Design of Experiments, sand casting of non ferrous alloy,
surface finish,
1. INTRODUCTION:
Casting remains as the most potent method for shaping of metals amongst
the various manufacturing methods, while it underwent a successful
transformation from art to science (Thareja 2005). More the advance
towards volumes, as in case of automotive castings, higher has been the
∗
Department of Mechanical Engineering, University Institute of Technology,, Punjab University Chandigarh. E-mail: [email protected]
**Department of Mechanical and production Engineering, Guru Nanak Dev Engineering College, Ludhiana E-mail
[email protected],, ;
*** Department of Metallurgical Engineering Punjab Engineering College, Chandigarh E-mail: [email protected]
Electronic copy available at: http://ssrn.com/abstract=1487542
growth stimuli. In modern foundries, green sand moulding method has
been widely used for especially small size automotive castings and
intricate ones. While remaining as a least expensive method, it gives
optimum quality thanks to a lower cost of sand, its ingredients and its
reusability. Green sand moulding derives its name due to presence of
moisture in it (Berth, 1990). Moisture is blended through sand Muller
using a mixture of silica sand, Bentonite clay, coal dust, wood floor,
dextrin powder, fire clay and water. The sand can be reused, till it exhibits
requisite bond development and other molding properties; albeit with
supplemented additions of Bentonite clay and water to account for due
losses attributed to ignition, evaporation and other kinds of degradation.
Innovative and strategic management of sand is essential for maintaining
due Quality of both process and product, ensuring all around
competitiveness (Thareja 2007).
Product Quality of a casting is a measure of its dimensional accuracy,
surface finish and soundness (Morgen, 1982). It depends upon the quality
of various constituents of green sand and structural properties of green
mould (Berth, 1990). It is also influenced by the metal or alloy, in terms of
their castability.
LM-6 aluminum alloy is a specification of cast Al-12% silicon alloy.
Characterised by good fluidity and resistance to hot tears, it can be easily
sand, pressure and gravity die cast. It is used for very thin, pressure tight
and intricate castings such as motor casings, meter casings and pump
impellers. It has very high resistance to atmospheric corrosion particularly
to saline water. That is why; it is mostly used for marine castings such as
cylinder head, inlet and exhaust manifolds, chemical and paint industry.
LM-6 possesses very high ductility making it useful for post casting metal
forming, which makes founding simplistic.
More versatility, through achievement of closer control of process
parameters and product characteristics, promises least costs and minimum
process rejections while targeting for consistent qualities.
Pignatiello and P. Ramberg (1985) have studied the heat treatment of leaf
springs of truck. The important factors and their interactions have been
studied and optimized for free height reduction to 8 inches standard for its
travel through heating furnace and forming machine, while bringing in
process improvement fo economics and robustness of process. Shan, et al.
(2002) have studied the significance of effects of magnetic field on
abrasive flow machining process on non ferrous materials. Syrcos (2003)
Electronic copy available at: http://ssrn.com/abstract=1487542
analyzed various significant process parameters of the die casting method
of AlSi9Cu13 aluminum alloy to obtain optimal settings of the die casting
parameters, in order to yield the optimum casting density of the AlSi9Cu13
aluminum alloy castings. The process parameters considered were: piston
velocity (first and second stage), metal temperature, filling time and
hydraulic pressure. The effects of the selected process parameters on the
casting density and the subsequent optimal settings of the parameters have
been accomplished using Taguchi’s method.
Tortum et al (2005) used Taguchi method to determine optimum
conditions for tire rubber in asphalt concrete using Marshall Test. The
various experimental parameters explored combinations of different tire
rubber gradations, mixing temperatures, aggregate gradations, tire rubber
ratios, binder ratio, compaction temperatures and mixing times.
Guharaja (2006) made an attempt to obtain optimal settings of the green
sand casting process in order to yield the optimum quality characteristics
of the spheroidal graphite (SG) cast iron rigid coupling. The effect of
selected process parameters i.e. green strength, moisture content,
permeability and mould hardness and its levels on the casting defects have
been accomplished using Taguchi’s parameter design approach. The result
indicated that the selected process parameters significantly affect the
casting defects of SG cast iron rigid coupling castings. An aggregate view
of such studies makes a case for deploying Taguchi method for surface
finish analysis.
In this paper an attempt has been therefore been made to find out the
optimal settings of the process parameters to get the better surface finish
of LM6 aluminium alloy. The Taguchi Design of Experiment technique
has been judicially employed as evidenced for its potency established in
aforementioned survey of literature. The confirmatory experiments have
also been carried out to verify the theoretical settings of the parameters.
2. TAGUCHI DESIGN APPROACH:
There are three approaches to Taguchi’s process which are namely System
design, Tolerance design and parameter design. Out of these three
approaches, parameter design was used in aluminium alloy (LM-6)
casting. It involves the following steps (Kackar, 1985):
1. Define the problem and determine the objective.
2. Choose relevant quality characteristics.
3. Brainstorm potential factors.
4. Choose important factors and their range & nos. of levels
5. Determine noise factors.
6. Develop the experimental design viz. Select the orthogonal arrays,
assign control factors and their interactions to column of inner array
and noise factors to outer array. Describe each trail condition and
decide their order and repetition.
7. Perform experimental trails and collect data.
8. Analyse the data and evaluate the optimum design.
9. Interpret the result.
10. Run the confirmatory experiment.
11. Implement the recommendations.
3. EXPERIMENTAL DESIGN:
The process parameters and their levels have been adapted from the
literature (Burns, 1986) for LM-6 aluminum alloy castings in synthetic
sand. The range of these parameters was selected on the basis of literature
review. The sand tends to imprint its net topography on surface of casting.
With this end in mind, and to effect a more robust conclusion to study, the
response parameter selected in this study was limited to surface roughness.
Coarser sand (represented by inverse of Grain Fineness value) thereby is
expected to result in poorer surface finish. Bentonite though provides more
strength and robustness to the mold, and cold dust higher smoothness
besides its impacts available to the extent of its combustion. Moisture,
though is considered as an interacting additive with bentonite, for later to
perform, influences the element of gas, which has a big, yet complex role
towards dictating required surface morphology of castings. Any increase
in Pouring temperature must increase fluidity of metal and therefore may
respond to surface conformance better. Will it be noticeable remains to be
determined by experimentation? The whole process is thus complex. So,
an experimental layout was proposed to be set at two distinct levels each
for five main parameters. Foseco handbook details requisite specifications
for synthetic sand compositions employable for Al-Si castings. The ranges
for Water (%age moisture) and bentonite are expressed to vary with in 3-4
% and 4 % respectively. Recommended AFS Grain Fineness is 80 to 100,
and AFS Permeability 40 – 50. The expected Green Compressive Strength
is 5 lb/ in2. The output necessarily mandates development of a sound
casting (Burns, 1986: 147). After considering recommended practices for
metal treatment and necessary precautions, as recommended in Foseco
handbook, the winners stand shaped castings (as shown in Fig 1) were
successfully cast using parameters detailed in Table I.
Fig 1 Dimensions of the pattern
The possibilities of specific interactions between the parameters are
detailed hereunder:
(i) Bentonite clay and AFS grain fineness number. (A × B): Amount of
bentonite depends upon grain fineness number. A bigger grain
qualitatively requires more bentonite clay due to larger surface area.
(ii) Bentonite clay and moisture. (A × C): Since sand-clay bonds are
developed by water, so amount of moisture depends directly on
amount of bentonite clay present in green sand mix.
Table I: Process parameters and their
levels
Designation
A
B
C
D
E
Level- LevelMain
paramete rs
1
2
Bentonite
4%
6%
clay
Grain
80
100
Fineness no.
Moisture
3%
4%
°
Pouring
710 C 730°C
Temp.
Coal dust
1%
1.5%
3.1 Selection of orthogonal Array:
Orthogonal arrays are special experimental designs constructed by
Taguchi that require only a small number of experimental trials to help
discover main factor effects. Orthogonal arrays are fractional factorial
designs and symmetrical subsets of all combinations of treatments in the
corresponding full factorial designs. The Orthogonal arrays facilitate the
experimental design process by assigning factors to the appropriate
column. For the selection of particular OA, the number of parameters, the
number of levels and possible interactions must be taken into
considerations. In order for an array to be a viable choice, the number of
rows in it must at least be equal to the degree of freedom required for the
case study. Degree of freedom (DOF) for all the above factors is ftotal. =
[5×1+2×1] = 7. Taguchi’s OA are selected on the basis of the condition
that the total DOF of a selected OA must be greater than or equal to the
total DOF required for the experiment. L8 (27) O.A. has eight number of
rows, and more DOF, is therefore selected. For this study, the utilization
of an L8(27) array of Taguchi method has reduced the number of
experimental configurations from (27) =128 (duly required for a full
factorial study) to 8 only. Figure 2 shows the linear graph of L8 orthogonal
array.
Figure
2
Linear
7
graph of L8 (2 )
4. RESULTS AND
PARAMETERS
ANALYSIS
FOR
OPTIMIZATION
OF
In conformance with Process parameters selected at respective levels as
tabulated in table I, founding experiments were made in the college metal
casting laboratory. Following a stipulated experimental route as shown in
table II, the aluminum castings were made in random order, dictated by L8
orthogonal array, using wooden pattern of dimensions as shown in figure
1. As the layout depicts, first casting was carried out using level 1 value
for all the parameters of table I. Similarly the casting 2 was carried out
using level 1 values of Bentonite and A.F.S Grain Fineness number and
level 2 values each for moisture, coal dust and temperature. In similar way
other experiments were carried out. The value of surface roughness was
measured with the help of Surfcoder available in the laboratory.
Table II:
Experimental layout for LM-6 aluminum alloy
castings
Casting
No. 1
1
2
3
4
5
6
7
8
Col. No.
2
3
4
5
6
7
Parameters
A
B
C
D
A×B
A×C E
Bentonite A.F.S.
Moisture
Coal Temp.
Clay (%) No.
(% age)
Dust (oC)
1
1
1
1
1
1
1
1
1
1
2
2
2
2
1
2
2
1
1
2
2
1
2
2
2
2
1
1
2
1
2
1
2
1
2
2
1
2
2
1
2
1
2
2
1
1
2
2
1
2
2
1
2
1
1
2
# The lower and upper level of the parameters has been shown as 1 and 2
respectively.
It was decided to take three surface readings randomly each at end
positions and center on all steps of the castings, as illustrated in figure 1
and tabulated in format P1, P2, P3 and Q1, Q2, Q3 respectively.
The Taguchi paradigm stipulates determination of a loss function to gauge
the deviation between the experimental and desired value of a
performance characteristic. The loss function is defined by the S/N ratio
(Signal to Noise) to determine the optimum parameters of casting process
of aluminium alloy. There are three types of categorisation possible for
S/N ratio analysis viz. Higher the better, the nominal the better and the
smaller the better. In this work, the characteristic measure was surface
roughness, whose lower (smallest) value should be able to label the
product as becoming superior. In other words, to obtain the optimal
parameters, the smaller the better type of response was considered. The
S/N ratio for smaller the better is given by
SN LB = − 10 log(
1 r
2
∑Y i )
r i =1
Average value of the surface roughness and those of S/N ratios for all
eight castings were calculated. The duly computed S/N ratios for
respective surface roughness are shown in table III.
Table III Surface roughness values
and S/N ratio
Casting
Surface
No.
Roughness
(µm)
1
5.676000
2
5.134333
3
7.097667
4
5.333167
5
5.094333
6
5.735333
7
6.702333
8
4.593000
S/N ratio
(dB)
-15.40
-14.43
-17.45
-14.73
-14.45
-15.41
-16.57
-13.30
4.1 Analysis of Variance for Raw and S/N data: Analysis of variance
(ANOVA) was performed to determine the relative significance of factors
in terms of their percentage contribution to the response. Results of pooled
ANOVA for both (i) Raw Data and the output data for Surface Roughness
(ii) Signal to Noise ratio are tabulated in Table IV and Table V.
Significant parameters have been highlighted. Noticeable surface response
in case of Moisture and Coal dust in case of both Surface roughness and
S/N ratio is evidenced from the results, as shown in Fig 3 also. Bentonite
addition responded in terms of improvement of surface finish when
increased pouring temperature was used.
Table IV: Pooled ANOVA of Raw Data. (Surface Roughness)
Pool
SS
Y
0.934
Y
3.264
N
10.685
Y
1.750
Y
1
f
V
F Ratio F*Ratio SS* P %
1 0.934 0.496
1 3.264 1.381
1+ 10.685 5.674** 5.526** 8.757 7.34
1 1.750 0.929
1
1
0.531
Y
0.526
1
N
N
N
11.840
29.999
6.048
7.817
1+ 11.840 6.288** 6.139** 9.912 8.31
7
1+ 6.048 3.212* 3.136* 4.120 3.45
1+ 7.817 4.151** 4.053* 5.889 4.94
B×P
Y
2.643
1
2.643
1.404
C×P
Y
0.766
1
0.766
0.407
D×P
Y
1.202
1
1.202
0.638
A×B×P Y
A×C×P Y
1.963
1
1.963
1.042
3.624
1
3.624
1.925
Y
5.003
1
5.003
2.657
Source
A
B
C+
D
A×B
A×C
E+
SSTI
P+
A×P+
E×P
SST2
e3
ep+
SST3
Y
59.065
60.257
82.932
119.322
0.526
15
32 1.883 43+ 1.928
47
0.279
75.96
100
Table V: Pooled ANOVA of S/N Data. (Surface Roughness)
Source
A
B
C+
D
×B
A×C
E+
ep+
Total
V F Ratio F*Ratio SS* P %
0.650 2.731
0.696 2.924
4.500 18.907 7.414** 3.893 32.42
0.769 3.231
0.684 2.874
Pool
Y
Y
N
Y
Y
SS
f
0.650 1
0.696 1
4.500 1+
0.769 1
0.684 1
Y
0.238
N
4.470 1+ 4.470 18.782 7.364** 3.863 32.17
5+ 0.607
35.41
12.007 7
100
1 0.238
-
Table VI: process parameters and their optimum
levels
Process
Parameters
Level
affecting
average
2
Level
affecting
variability
-
Moisture percentage
(C)
2
2
Coal Dust
percentage (E)
1
1
Bentonite clay (A)
(a)
(b)
(c)
(d)
(e)
Fig 3: Response plots for surface roughness (for
significant factors only).
(a), (b) & (c) show effects of A, C & E on process
average and (d) and (e) shows effects of C & E on S/N
data.
The factors of LM-6 aluminum alloy sand castings experiment for surface
roughness can be classified as:
It is found that the optimal parameter combination of Green sand casting
process for best surface finish corresponded to a Bentonite clay addition to
6% (Level-2), a moisture level of 4% (Level-2) and Coal dust addition of
1% (Level-1). The response values in terms of levels impacting either
Average and/or variability are summarized in table VI. Thus, a state of A2
C2 E1 is recommended for achieving highest surface finish for LM-6
aluminium alloys castings made in Green sand. Higher percentage of
Bentonite clay was found to affect the average of the surface finish only.
The effect is attributable to lower mould wall degradation as a result of
bentonite strengthening. Higher percentage of water was found to affect
the phenomenon of mould binding and finishing resulting in better finish
of produced castings. It was found to affect both the average and
variability of surface finish. Lower percentage of Coal dust was found to
optimally produce best surface finish on castings. It was also found to
affect both the average and variability of the surface finish. The metal
pouring temperature and Grain fineness number showed no effect on
surface finish, so a lower level of these factors for metal superheat and
also of AFS no. can be selected.
4.1.1 Estimation of Optimum performance characteristics:
The averages of the levels of factor A, C and E are:
A 1 = 5 . 811
C 1 = 6 . 143
A 2 = 5 . 532
C
2
= 5 . 199
E 1 = 5 . 174
E
2
T = 5 . 671
= 6 . 168
So, optimum levels are:
A 2 = 5 . 532 μ m
C
= 5 . 199 μ m
2
E 1 = 5 . 174 μ m
The estimated average results, when the three control factors are at their
better level is:
μA2,C2,E1 = A + C + E − 2 T
=5.532+5.199 + 5.174 –2 (5.671)
= 4.563 µm
2
2
1
……..(1)
The 90% confidence interval (C.I.) for the population and the confirmation
experiments of 2 parts is:
Calculated as:
(a) For population:
F
CIpop= ±
α ( fi &
fe )
× V
e
…….
n eff
(2)
(b) For confirmation experiment:
CICE =
±
F α ( fi
Where, neff. =
& fe )
× Ve ×
1
1
+
n eff
r
…….(3)
Total number of observations
1+ (Total degree of freedom associated with items used in
estimating μ)
48
By substituting the following: F0.90 (1& 43) = 2.831, Ve = 1.928; n eff. =
=4; r = 12, CI is
1+3
Calculated to be ± 0.674. Therefore the 90% confidence interval should be
given by:
3.609 micron < μCE < 5.517 micron
4.1.2 Confirmation Experiments:
As part of the validation; two samples were cast at the optimum condition
as above; with surface roughness at position 1 & 2 determined as 5.134,
4.573, 5.218, 3.911, 5.176, 6.109; 6.014, 4.198, 5.368, 5.140, 4.366 and
5.004 micron respectively. The samples have been evaluated following the
same criteria used for the original experiments. The average of the sample
has been found to be 5.018 micron, which is within the confidence
interval. The expected reduction in surface roughness has been found to be
11.5%.
Avg. Surface
Roughness (micron)
8
7
6
5
4
3
2
1
0
11.5 % Reduction
5.671
5.018
1
Before Exp.
2
After Exp.
Figure 4 Expected reduction in surface
roughness
CONCLUSIONS:
The study investigated the optimisation of Sand Casting process factors
and levels using Taguchi Method. The results are summarized as follows:
1. Taguchi Technique can be effectively deployed to get desired
Quality improvement @ austere experimentation. In presence work
the metric of Quality improvement was chosen to be surface finish,
and only 8 experiments sufficed against 128 necessitated in
conventional circumstances.
2. The optimal parameter combination of Green sand casting process
for best surface finish corresponded to a Bentonite clay addition to
6%, a moisture level of 4% and Coal dust addition of 1%. Therefore,
A2 C2 E1 is recommended for achieving best surface finish for LM-6
aluminium alloys castings in Green sand.
3. The confirmation experiment was conducted to validate the
experimental work done. It showed expected improvement of 11.5%
in average surface finish of the castings.
4. The predicted optimal range for the confirmation experiment of two
experiment is given by 3.609 micron < μCE < 5.517 micron.
REFERENCES
1. Bendell A., (1988),"Introduction to Taguchi methodology," Taguchi
methods: Proceedings of the 1988 European Conference, London,
Elsevier applied science, pp. 1-14,.
2. Berth, Mary and Pedecine, L.J., (1990), “Sand handling systems
affect casting produced in green sand,” Modern casting, vol-II., pp.
42 to 44.
3. Burns T.A. (1986), " The Foseco’s foundryman’s handbook, facts,
figures and formulae” Pergamon press, New York, pp 128 & 147 to
148.
4. Flinn R.A., (1981) “Why castings fail or succeed?”AFS Translation,
Vol.89, pp. 519 to 528.
5. Guharaja S. , A. Noorul Haq and K. M. Karuppannan, (2006),
“Optimization of green sand casting process parameters by using
Taguchi’s method”, The International Journal of Advanced
Manufacturing Technology, Volume 30, Numbers 11-12, pages1040-1048
6. Heine W.R., Looper R.C., Jr. Rosenthal, C.P., “Principles of metal
castings” Tata-McGraw hill, 1988.
7. Kackar N.Raghu, (1985) “Off-line quality control, parameters
design, and the Taguchi method” Journal of quality Technology,
Vol. 17, No. 4, pp. 176 to 188.
8. Kobu Y.Nakata, K.Akita & Gouwens, R.R., (1973) “Moulding
unbounded sand with vacuum- The V-process”, AFS Translation,
Vol.81, pp. 529-544.
9. Morgen A.D., (1982) “Highest quality casting - Which moulding
process?” Foundry Trade Journal, pp. 611 to 622.
10.
Petterson W. Cleland R.L., (1965) “Surface smoothness of
casting”, Modern casting, , pp. 857 to 864.
11.
Pignatiello, J. Jr. and Ramberg, J.S. (1985), Discussion on
Kackar’s article, Journal of Quality Technology, vol..17, pp.198-206.
12.
Phadke, S. M., 1989. Quality Engineering Using Robust
Design, Prentice Hall, Englewood
Cliffs, N.J
12.
Ross, J.P., “Taguchi technique for quality engineering,” New
York: McGraw-Hill,
13.
Shan H.S, Singh Sehijpal, and Kumar Pardeep, (2002)
“Parametric optimisation of magnetic-field-assisted abrasive flow
machining by the Taguchi method,” Quality and Reliability
Engineering International Journal, vol. 18, pp.273 to 283.
14.
Syrcos G. P. (2003), “Die casting process optimization using
Taguchi methods” Journal of Materials Processing Technology ,
Volume 135, Issue 1 , Pages 68-74
15.
Thareja
Priyavrat, Thareja Mannu, (2005) “Competence Thru’ a competence
protocol”, FOUNDRY, Vol XVII, Nov 4, Jul/Aug 100th issue, pp.1925
16.
Thareja
Priyavrat, (2007), “Competent Foundry@ Creativity,” Transactions
2007, Proceedings of the 55th Indian Foundry Congress, Agra, Feb.
2-4, pp.420-431
17.
Taguchi,
G., El Sayed, M. & Hsaing, C. (1989). Quality engineering and
production systems. New York: McGraw-Hill.