Determining the Optimum Green Sand Casting Process Parameters Using Taguchi 2012 Journal of the...

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Determining the optimum green sand casting processparameters using Taguchi's methodA. Kumaravadivel a , U. Natarajan b & C. Ilamparithi aa Department of Mechanical Engineering , Sudharsan Engineering College , Pudukkottai ,Tamilnadu , Indiab Department of Mechanical Engineering , ACCE&Tech. , Karaikudi , Tamilnadu , IndiaPublished online: 09 Mar 2012.

To cite this article: A. Kumaravadivel , U. Natarajan & C. Ilamparithi (2012) Determining the optimum green sand castingprocess parameters using Taguchi's method, Journal of the Chinese Institute of Industrial Engineers, 29:2, 148-162, DOI:10.1080/10170669.2012.664789

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Journal of the Chinese Institute of Industrial Engineers

Vol. 29, No. 2, March 2012, 148–162

Determining the optimum green sand casting process parameters using

Taguchi’s method

A. Kumaravadivela*, U. Natarajanb and C. Ilamparithia

aDepartment of Mechanical Engineering, Sudharsan Engineering College, Pudukkottai,Tamilnadu, India; bDepartment of Mechanical Engineering, ACCE&Tech.,

Karaikudi, Tamilnadu, India

(Received May 2011; revised December 2011; accepted January 2012)

This study demonstrates optimization of sand casting process parameters using Taguchi’s design ofexperiments method. The process parameters considered are moisture content, permeability, loss onignition, compressive strength, volatile content, vent holes, pouring time, pouring temperature, andmold pressure. An attempt has been made to obtain optimal level of the process parameters in order togenerate the optimum quality characteristics of the cast iron flywheel castings. An orthogonal array,the signal-to-noise ratio, and analysis of variance are used to analyze the effect of selected processparameters and their levels on the casting defects. The result indicates that the selected processparameters significantly affect the casting defects of cast iron flywheel castings. A confirmation run isused to verify the results, which indicates that this method is more efficient in determining the bestcasting parameters for flywheel castings.

Keywords: green sand casting; casting defects; Taguchi’s method; process parameters; flywheel

1. Introduction

Green sand casting process, in general, involves alarge number of parameters affecting the variouscasting quality features of the product. Green sandcasting gives enough green strength to get dimen-sional stability and to provide excellent surfacefinish and better collapsibility during the knockout.A large number of experimental investigationslinking green sand casting parameters with castingquality have been carried out by researchers andfoundry engineers over the past few decades [2]. Ithas been recognized that the design of green sandcasting parameters impacts casting quality. Thecasting process has a large number of parametersthat may affect the quality of castings. Some ofthese parameters which affect the quality arecontrollable, while others are uncontrollable.

The purpose of the process development is toimprove the performance of the process relating tothe customer needs and expectations. The processdevelopment can be achieved through experimen-tation and the aim of the process is to reduce andcontrol the variation of a process. Subsequently,decisions must be made concerning which param-eter affects the performance of the process. The lossfunction quantifies which design factor influencesthe average and variation of performance of theprocess. By properly adjusting the factors, thevariations of the process are reduced and therebythe losses can be minimized [24].

Up to now, the gradient search method, thefinite element method (FEM)-based neural networkmethod and the Taguchi method are the optimiza-tion methods applicable to the design of green sandcasting process parameters [12,15]. Taguchi [29]and Masters et al. [17] have introduced several newstatistical tools and concepts of quality improve-ment that depend heavily on the statistical theoryof experimental design. Some applications ofTaguchi’s methods in the foundry industries haveshown that the variation in casting quality, causedby uncontrollable process variables, can be mini-mized [8,21]. Raisinghani [23] and Ross [24]reported that the Taguchi approach is suitable inusing experimental design for (a) designing anddeveloping products/processes so as to be robust tocomponent variation; (b) designing products/pro-cesses so as to be robust to environmental condi-tions; and (c) minimizing variation around a targetvalue. The Taguchi technique appears to be anideal tool for continuous and rapid quality productdesign, and it becomes easier and more productivefor today’s highly competitive internationalmarkets.

2. Literature review

The majority of published articles on the optimi-zation of parameters based on the Taguchi’smethod are reviewed below.

*Corresponding author. Email: [email protected]

ISSN 1017–0669 print/ISSN 2151–7606 online

� 2012 Chinese Institute of Industrial Engineers

http://dx.doi.org/10.1080/10170669.2012.664789

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During the 1990s, a lot of developments hadtaken place in the foundry process [2,7]. Some ofthese programs are able to simulate the behavior ofthe molten metal close to reality, as the researchersstudied the behavior of the molten gray cast ironduring the filling of different gating systems byoptical means and correlated the measurements toobtain the behavior by some simulators. Guharajet al. [9] have made an attempt to obtain optimalsettings of green sand casting parameters usingTaguchi method. Chang and Liao [5] have pre-sented a gray-based Taguchi method to discoverthe optimal design of the rifle muzzle flash reducer.According to Byrne and Taguchi [4], the parame-ters, which exert a great deal of influence on the diecasting process, can be adjusted to varying levels ofintensity, so that some settings can result inrobustness of the manufacturing process. Baruaet al. [3] used the Taguchi’s method to optimizethe effects of the selected process parameters on themechanical properties of alloy casting of theVacuum V-casting process. Jeyapaul et al. [11]used the Taguchi concept to optimize the sandcasting parameters. Enright and Prince [6] havedeveloped a simple mathematical model to studythe effects of liquid metal flow, transient heattransfer, and foam degradation during castingprocess. Noorul Haq et al. [20] presented theconcepts to optimize the CO2 casting parametersusing Taguchi. Jolly et al. [13] analyzed numericalsimulators based on FDM and FEM methods andprovided powerful means of analyzing variousphenomena occurring during the casting process.Juran et al. [14] stated that control factors are theselected independent variables of the experiment,which have different effects on the response vari-ables when adjusted to different levels. Masterset al. [17] have described a robust design methodfor reducing cost and improving quality in analuminum remelting process. An experimentalinvestigation into the process parameter effect ispresented to determine the optimum configurationof design parameters for performance, quality, andcost. Muzammil et al. [19] made a study for theoptimization of a gear blank casting process usingTaguchi’s robust design technique and they dem-onstrated that the casting process involves a largenumber of parameters affecting the various castingquality features of the product. Papai and Mobley[22] made detailed temperature measurements indie casting dies, from which the values of param-eters like the heat transfer coefficient at theinterface casting–die are calculated. Syrcos [28]analyzed various significant process parameters ofthe die casting method of aluminum alloy andmade an attempt to obtain optimal settings of thedie casting parameters in order to generate the

optimum casting density of the aluminum alloycastings. Antony et al. [1] used a systematicframework for quality improvement and gainingbusiness excellence, and the Six Sigma philosophyhas become the paradigm of the industrial world inrecent years.

Apart from the casting process, the Taguchimethod can be used for other manufacturingprocesses like milling, radial forging, grinding,and the machining of composites [8,26,27], respec-tively. Based on the above discussions, researchersattempted to optimize the sand casting processparameters by conducting analysis of variance(ANOVA) experiments on Taguchi’s concept tominimize the defects in the casting process.

The study is further segmented as follows.Section 3 explains the selected parameters withtheir defect levels. Section 4 defines the selection ofan orthogonal array (OA). In Section 5, analysisphase of Taguchi with a case study is explained.Confirmation of experiments and results is pro-vided in Section 6, and Section 7 concludes theresearch findings.

3. Process parameters of green sand castings

The focus of this article is on the robustness of thegreen sand casting process and the case company isa leading automobile foundry located in southIndia. The basic steps for achieving the abovetarget are summarized below [25,28]:

(1) To select the most significant parametersthat cause variations in the qualitycharacteristics.

(2) Casting defects have been selected as themost representative quality characteristicsin the green sand casting process, as it isrelated to many internal defects (shifts, sandinclusion, blow holes, sand drop, slag, etc.).The target of the green sand casting processis to achieve ‘‘lower casting defects’’ whileminimizing the effect of uncontrollableparameters.

(3) Make the green sand casting process underthe experimental conditions dictated by thechosen OA and parameter levels. Based onthe experimental conditions, collect thedata.

(4) An ANOVA table is generated to determinethe statistical significance of the parameters.

Response graphs are plotted to determine thepreferred levels for each parameter.

(5) The optimum settings of the control param-eters and predict the results of each of theparameters at their new optimum levels.

Journal of the Chinese Institute of Industrial Engineers 149

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(6) Verify the optimum settings result in thepredicted reduction in the casting defects.

A cause-and-effect matrix is drawn to identifythe casting process parameters with respect to keyprocess input variables (KPIVs) that may influencegreen sand casting defects, as shown in Table 1.

Cause-and-effect matrix relates the key inputsto the key outputs (customer requirements). Thekey outputs are ranked according to their impor-tance, while the key inputs are ranked in terms oftheir relationship to that of the key outputs. In thematrix, a factor of importance for each parameteris ranked orderly and every listed input parameteris correlated to every output parameter. Finally, atotal value for each parameter is obtained bymultiplying the rating of importance with the valuegiven to the parameters and by adding across foreach parameter. The results obtained with thecause-and-effect matrix can be used for optimiza-tion. The KPIVs are listed on the left-hand side,while the key process output variables (KPOVs) arelisted on the top right-hand side of the diagram. Incertain cases, the KPIVs of one process are theKPOVs for the next process; for example, moisturecontent and operator unawareness.

The process parameters can be listed in sixcategories as follows:

(1) Mold machine related parameters(2) Cast metal related parameters(3) Green sand related parameters(4) Mold-related parameters(5) Shake out related parameters(6) Man process related parameters

To visualize the effect of process parameters onthe casting defects of cast iron flywheel castings,following parameters are selected:

. Moisture content

. Permeability

. Loss on ignition

. Compressive strength

. Volatile content

. Vent holes

. Pouring time

. Pouring temperature

. Mold pressure

(a) The range of moisture content is taken as3.0–3.6%, (b) the range of permeability 120–190,(c) the range of loss on ignition 3.5–5.0%, (d) therange of compressive strength number 1.0–1.35 kg/cm2, (e) the range of volatile content 2.1–3.5%, (f)the range of vent holes 8–10, (g) the range ofpouring time 45–48 s, (h) the range of pouringtemperature 1400–1440�C, and (i) the rangeof mold pressure 5–7 kg/cm2. The number oflevels for each control parameter defines the

experimental region. For each control factor,three levels are selected, out of which, one level isusually the starting level. The selection of param-eter levels for the process should cover a wideexperimental region. The sensitivity to noise factorsdoes not usually change but there are small changesin the control factor settings. Therefore, eachparameter is analyzed at different levels based onthe behavior of the process parameters. From theselected parameters, the significant interactionbetween them is also considered. The selectedgreen sand casting process parameters, along withtheir ranges, are given in Table 2.

4. Selection of an OA

Experience reveals that non-linear behavior amongthe parameters of a sand casting process can onlybe determined if more than two levels of parame-ters used [17]. Before selecting a particular OA tobe used for conducting the experiments, two pointsmust be considered.

(1) The number of parameters and interactionof interest.

(2) The number of levels for the parameters ofinterest.

In accordance to the study conducted [24] toknow the parameter interactions, it is inferred thatthere are significant interactions of moisture con-tent with permeability and moisture content withloss on ignition. Based on the literatures and thesynthesized data of the foundry, with the foundryman’s experience, moisture content variation withpermeability of the sand (A�B) and the loss onignition of the sand (A�C) are taken to investigatethe two factor interaction effects on casting rejec-tion. The total degree of freedom (DOF) for ninefactors, each in three levels and two second-orderinteraction, is 26¼ (9� (3� 1)þ 2(2� 2)). Hence, athree-level OA, with at least 26 DOFs, has to beselected. The L27 OA having 26 DOFs is selectedfor this study.

5. Case study – Taguchi

Once the parameters and parameter interactionsare assigned to a particular column of the selectedOA, the factors at different levels are assigned foreach trial. The assigned experimental array isshown in Table 3. The castings of flywheel aremade as per the trial conditions given in Table 3.The experiments are conducted thrice for the sameset of parameters using single repetition randomi-zation technique [24,19]. The percentage of defectfor each repetition and average casting defects arecalculated for each trial condition. The casting

150 A. Kumaravadivel et al.

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Table

1.Cause-and-effectmatrix.

Importance

estimationasscale

forprocess

from

customer

33

35

55

53

53

13

15

33

3

S.no.

12

34

56

78

910

11

12

13

14

15

16

17

KPIV

KPOV

Msrun

Coldshut

Hottears

Crack

Shrinkage

Slaginclusion

Coreshift

Timeofcycle

Sandinclusion

Scabs

Outsandwasher

Moldshift

Swells

Blowholes

Pourisityandpinholes

Warpage

Dirt

Total

1Delayin

pouringtime

33

00

03

03

35

00

30

03

084

2Interruptedpouring

33

00

03

03

30

00

30

03

069

3Toolongpouring

00

00

00

01

10

00

00

03

015

4Highpouringrate

00

11

30

00

30

00

30

01

044

5Toohighheightofladle

00

00

01

03

10

00

10

00

020

6Dirty

ladles

00

00

03

00

10

00

10

00

536

7Strainer

orfilter

core

notused

00

00

05

00

00

00

00

00

025

8Repeateduse

ofladle

withoutlinings

00

00

05

00

00

00

03

00

040

9Im

proper

skim

ming

00

00

05

00

00

10

00

00

026

10

Insufficientpouringtemperature

00

55

35

01

30

00

05

00

0123

11

Operatorunawareness

00

00

03

00

50

00

00

00

040

12

Low

compactability

00

00

00

00

50

00

00

30

034

13

Use

ofhotsand

00

00

00

00

30

00

00

10

018

14

Improper

moisture

00

11

00

00

50

00

00

00

033

15

Low

green

strength

00

00

00

00

30

00

00

35

348

16

Low

flowability

11

00

00

00

10

00

00

30

020

17

Insufficientbinder

00

00

00

00

10

30

00

10

011

18

Poorgrain

distribution

00

11

03

00

13

30

33

30

067

19

Insufficientmullingtime

00

00

05

01

00

00

03

00

043

20

Highmoisture

content

00

00

05

00

50

00

00

00

040

21

Operatorunawareness

00

00

05

00

50

00

00

00

150

22

Sharp

comers

13

11

11

10

10

11

00

00

145

(continued

)


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Table

1.Continued.

Importance

estimationasscale

forprocess

from

customer

33

35

55

53

53

13

15

33

3

23

Copedragmismatch

33

00

31

30

10

13

00

00

377

24

Designparameters

11

00

31

10

30

01

00

00

152

25

Poorfinishingandcleaning

33

00

00

33

50

33

03

00

097

26

Gatingparameters

55

00

53

00

30

00

00

15

3112

27

Changein

runner

dim

ension

00

00

30

01

00

00

00

50

033

28

Changein

raiser

dim

ensions

00

00

10

01

00

00

00

30

017

29

Insufficientairpressure

00

00

00

00

10

00

00

30

014

30

Improper

ventholes

00

03

50

05

00

00

05

50

095

31

Uneven

stripping

00

00

00

00

30

00

00

00

015

32

Improper

ramming

00

55

00

03

35

00

05

00

5113

33

Insufficientturbulence

–gatingsystem

00

00

05

00

00

00

00

00

050

34

Insufficientpermeability

00

00

00

00

00

00

05

30

034

35

Moisture

content

00

00

05

03

00

00

03

50

073

10

Insufficientpouringtemperature

00

55

35

01

30

00

05

00

0123

37

Operatorunawareness

00

00

03

00

50

00

00

00

040

66

66

42

85

135

260

40

87

315

48

12

24

14

185

126

60

66


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defects are the ‘‘lower the better’’ type of quality

characteristics. Lower the better signal-to-noise

(S/N) ratios are computed for each of the 27

trials given in Table 4. Taguchi method uses the

S/N ratio instead of the average to convert the trial

result data into a value for the characteristic in the

optimum setting analysis. The S/N ratio replicates

both the average and the variation of the quality

characteristics.For the case of minimizing the performance

characteristic, S/N ratio can be calculated as:

SNi ¼ �10 logXN1

i¼1

Yi2=Ni

!ð1Þ

where Yi is the response value of observation in ithtest and Ni the number of trials in ith test.

For example, for trial no. 1, the S/N ratio isgiven in Equation (1): S/N ratio¼�10 log�[(7.682þ 6.582þ 6.872)/3)]¼�16.97. The responseof casting defects referring to the average value ofthe performance characteristics for each parameterat different levels is calculated and given in Table 5.

5.1 Raw data analysis of experimental results

The average values of the response at each param-eter level are obtained by adding the results of alltrail conditions at the level considered and then

Table 3. Experimental L27 OA.

Trailno.

Moisturecontent (%) Permeability

Loss onignition(%)

Compressivestrength(kg/cm2)

Volatilecontent(%)

Ventholes(Nos)

Pouringtime (s)

Pouringtemperature

(�C)

Moldpressure(kg/cm2)

1 3 120 3.5 1 2.1 8 45 1400 52 3 120 4.25 1.175 2.8 9 46.5 1420 63 3 120 5 1.35 3.5 10 48 1440 74 3 155 3.5 1.175 2.8 9 48 1440 75 3 155 4.25 1.35 3.5 10 45 1400 56 3 155 5 1 2.1 8 46.5 1420 67 3 190 3.5 1.35 3.5 10 46.5 1420 68 3 190 4.25 1 2.1 8 48 1440 79 3 190 5 1.175 2.8 9 45 1400 510 3.3 120 3.5 1 2.8 10 45 1420 711 3.3 120 4.25 1.175 3.5 8 46.5 1440 512 3.3 120 5 1.35 2.1 9 48 1400 613 3.3 155 3.5 1.175 3.5 8 48 1400 614 3.3 155 4.25 1.35 2.1 9 45 1420 715 3.3 155 5 1 2.8 10 46.5 1440 516 3.3 190 3.5 1.35 2.1 9 46.5 1440 517 3.3 190 4.25 1 2.8 10 48 1400 618 3.3 190 5 1.175 3.5 8 45 1420 719 3.6 120 3.5 1 3.5 9 45 1440 620 3.6 120 4.25 1.175 2.1 10 46.5 1400 721 3.6 120 5 1.35 2.8 8 48 1420 522 3.6 155 3.5 1.175 2.1 10 48 1420 523 3.6 155 4.25 1.35 2.8 8 45 1440 624 3.6 155 5 1 3.5 9 46.5 1400 725 3.6 190 3.5 1.35 2.8 8 46.5 1400 726 3.6 190 4.25 1 3.5 9 48 1420 527 3.6 190 5 1.175 2.1 10 45 1440 6

Table 2. Process parameters with their ranges and values at three levels.

Parameter designation Process parameters Range Level 1 Level 2 Level 3

A Moisture content (%) 3–3.6 3 3.3 3.6B Permeability 120–190 120 155 190C Loss on Ignition (%) 3.5–5 3.5 4.25 5D Compressive strength (kg/cm2) 1–1.35 1 1.175 1.35E Volatile content (%) 2.1–3.5 2.1 2.8 3.5F Vent holes (Nos) 8–10 8 9 10G Pouring time (s) 45–48 45 46.5 48H Pouring temperature (�C) 1400–1440 1400 1420 1440

I Mold pressure (kg/cm2) 5–7 5 6 7


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dividing by the number of data points added.

Those for parameter A, the levels total of the

response at A1 and A2 are calculated and it is given

in Equation (2), as explained by Montgomery [18].

A1 ¼ y11 þ y12 þ y13½ � þ y21 þ y22 þ y23½ �

¼ y31 þ y32 þ y33½ � þ y41 þ y42 þ y43½ �

þ y51 þ y52 þ y53½ �

þ y61 þ y62 þ y63½ � þ y71 þ y72 þ y73½ �

þ y81 þ y82 þ y83½ �

þ y91 þ y92 þ y93½ � ð2Þ

The average values of the responses at A1 and

A2 are given in Equation (3)

A 1ð Þ ¼A1

27and A 2ð Þ ¼

A2

27ð3Þ

The average values of casting defects and S/N

ratios for each parameter at different levels are

plotted in Figures 1 and 2, respectively.It is clear from Figures 1 and 2 that the sand

casting defects are minimum at third level of

moisture content (A3), first level of permeability

(B1), first level of loss on ignition C (C1), first level

of compressive strength (D1), first level of volatile

content (E1), third level of vent holes (F3), first level

of pouring time (G1), first level of pouring temper-

ature (H1), and first level of mold pressure (I1).

The S/N ratio is also maximum at the same levels of

the parameters. In order to study the significance

of the parameters, three-way ANOVA is performed

for casting defects and S/N ratios and the results

are shown in Table 6.The F-ratio value can be found using the ratio

of mean square of a factor to variance of error is

given in Equation (5). It could be seen from the

F-ratio value result that the significant factors are

the control factors in the order of B (permeability),

E (volatile content), A (moisture content), I (mold

pressure), H (pouring temperature), C (loss on

ignition), G (pouring time), D (pouring time), and F

(vent holes). Furthermore, permeability, volatile

content, moisture content, and mold pressure are

the most significant parameters in the green sand

casting process, since they contribute a greater

percentage than all other control factors. In this

study, the total variation present in the process is

decomposed into following components given in

Equation (4):

(1) Variation due to factors A, B, C, D, E, F, G,

H, I(2) Variation due to factors A�B and G�H.(3) Variation due to error

Total variation

SST ¼SSA þ SSB þ SSC þ SSD þ SSE þ SSF

þSSG þ SSH þ SSI þ SSA�B þ SSA�C

� �ð4Þ

Variation due to error

SSe¼SST

�SSAþSSBþSSCþSSDþSSEþSSF

þSSGþSSHþSSIþSSA�BþSSA�C

" #

¼ 1:0004 ð5Þ

Table 4. Casting defects values and S/N ratio againsttrail numbers.

S. no Trial 1 Trial 2 Trial 3 Average S/N ratio

1 7.68 6.58 6.87 7.04333 �16.97452 7.12 6.42 7.68 7.07333 �17.01553 8.26 7.28 7.78 7.77333 �17.82364 8.61 8.61 7.21 8.14333 �18.24455 6.81 8.31 6.24 7.12000 �17.11446 7.52 6.38 8.34 7.41333 �17.45107 7.86 7.34 8.12 7.77333 �17.81978 6.28 7.61 7.26 7.05000 �16.99149 7.96 7.67 6.21 7.28000 �17.290410 7.24 8.34 6.36 7.31333 �17.335311 7.54 9.10 6.12 7.58667 �17.711412 7.86 6.54 7.86 7.42000 �17.438513 8.65 6.24 7.12 7.33667 �17.389214 8.68 8.24 6.86 7.92667 �18.023215 8.26 7.31 8.46 8.01000 �18.089716 6.62 6.32 8.24 7.06000 �17.037617 7.34 8.24 7.66 7.74667 �17.792318 8.24 8.36 8.24 8.28000 �18.360819 7.12 6.21 6.64 6.65667 �16.478720 6.52 6.12 8.13 6.92333 �16.874221 6.64 6.48 7.24 6.78667 �16.643222 6.94 8.24 7.12 7.43333 �17.449623 8.12 8.36 8.24 8.24000 �18.319224 8.24 8.12 6.76 7.70667 �17.770125 6.74 7.84 7.78 7.45333 �17.466926 7.26 8.21 8.32 7.93000 �18.001127 8.21 6.12 6.84 7.05667 �17.0370

Table 5. Average values of casting defects and S/Nratios at different levels.

Factors

Level 1 Level 2 Level 3

Castingdefects

S/Nratios

Castingdefects

S/Nratios

Castingdefects

S/Nratios

A 7.408 �17.41 7.631 �17.69 7.354 �17.34B 7.175 �17.14 7.703 �17.76 7.514 �17.53C 7.357 �17.36 7.511 �17.54 7.525 �17.54D 7.430 �17.43 7.457 �17.49 7.506 �17.52E 7.259 �17.25 7.561 �17.58 7.574 �17.61F 7.466 �17.48 7.466 �17.48 7.461 �17.48G 7.435 �17.44 7.444 �17.47 7.513 �17.53H 7.337 �17.35 7.543 �17.57 7.503 �17.53I 7.361 �17.37 7.413 �17.42 7.619 �17.65


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Table 6. ANOVA for Casting defects and S/N ratio at 95% Confidence Limits.

Sum of squares (SS) Degrees of freedom (�) Variance (V) F-ratioa

SourceCastingdefects S/N ratio

Castingdefects S/N ratio



A 0.3885 0.6049 2 2 1.1942 0.3024 10.497 16.345B 1.2891 1.7538 2 2 0.6445 0.8769 34.837 47.400C 0.1563 0.2086 2 2 0.0781 0.1043 4.2216 5.6378D 0.0266 0.0363 2 2 0.0133 0.0181 0.7189 0.9783E 0.5725 0.6959 2 2 0.2862 0.3479 15.470 18.805F 0.0001 0.0000 2 2 0.0000 0.0000 0.0000 0.0000G 0.0328 0.0408 2 2 0.0164 0.0201 0.8864 1.0864H 0.2269 0.2481 2 2 0.1134 0.1244 6.1297 6.7243I 0.3346 0.4240 2 2 0.1643 0.2120 9.0432 11.459A�B 0.5760 0.8152 4 4 0.1440 0.4076 7.7837 22.032A�C 0.6299 1.0175 4 4 0.4074 0.5087 22.020 40.696Error (e) 1.0004 1.0004 54 54 0.0185 0.0185 1.0000 1.0000Total 5.2337 6.8457 80 80

a¼Level of confidence – alpha (a¼ 0.05)

7.408

7.631

7.354

7.175

7.703

7.514

7.357

7.511

7.525

7.437.457

7.506

7.259

7.5617.574

7.466

7.4667.461 7.435

7.444

7.513

7.337

7.5437.503

7.3617.413

7.619

6.9

7

7.1

7.2

7.3

7.4

7.5

7.6

7.7

7.8

A1A2A3 B1B2B3 C1C2C3 D1D2D3 E1E2E3 F1F2F3 G1G2G3 H1H2H3 I1 I2 I3

Average value of casting defects for different parameters at different levels

Y Axis- % of casting defcts X Axis - Factors at different level

Figure 1. Average values of percentage of casting defects for each parameter at different levels.

-17.41

-17.69

-17.34

-17.14

-17.76

-17.53

-17.36

-17.54-17.54

-17.43

-17.49

-17.52

-17.25

-17.58-17.61

-17.48 -17.48

-17.48

-17.44-17.47

-17.53

-17.35

-17.57-17.53

-17.37

-17.42

-17.65

-17.9

-17.8

-17.7

-17.6

-17.5

-17.4

-17.3

-17.2

-17.1

-17

-16.9

-16.8A1A2A3 B1B2B3 C1C2C3 D1D2D3 E1E2E3 F1F2F3 G1G2G3 H1H2H3 I1 I2 I3

Average value of S / N ratio for different values at different levels

Y Axis - % of S/Nratio X Axis - Factors at different level

Figure 2. Average values of S/N ratios for each parameter at different levels.


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The DOF for the error (�e) is given inEquation (6):

Total degrees of freedom

�T ¼��A þ �B þ �C þ �D þ �E þ �F þ �G

þ�I þ �A�B þ �A�C�

�e ¼ �T ��A þ �B þ �C þ �D þ �F þ �Gþ

�H þ �I þ �A�B þ �A�C

� �¼ 54

ð6Þ

The result of the ANOVA for casting defectsand S/N ratio are shown in Table 6. From Table 6,it is clear that those parameters A, B, E, and Isignificantly affect both the mean and variation inthe casting defects. The expected amount of sum ofsquares (SS0) for each factor is computed usingvariance. The percent contribution (P) for eachfactor is calculated using expected amount of sumof squares (SS0).

5.2 Results of ANOVA

The ANOVA is used to analyze the results of theconducted experiments. Taguchi method cannotjudge and determine the effect of individual param-eter on the entire process. The significant factorsand/or their interactions are identified for varioustrial conditions and the parameters which signifi-cantly influence the casting defects. However, somemore information is required to conclude with anoptimum setting of parameters [29].

The ANOVA can, however, be useful fordetermining influence of any given input parameterfrom a series of experimental result by design ofexperiment.

5.2.1 Pooling

If the calculated F-ratio for a parameter is less thanthe tabulated F-ratio (3.20) at a stated confidencelevel (95% confidence level), the effect of theparameter is insignificant and the parameter ispooled. If the parameter is pooled, the sum ofsquares due to the parameter is added to the errorsum of squares and other ANOVA calculations aremodified in Equations (7)–(9). If the parameter A ispooled,

SSe Pooledð Þ ¼ SSe þ SSA ð7Þ

fe Pooledð Þ ¼ fe þ fa ð8Þ

Ve þ Pooledð Þ ¼ SSe Pooledð Þ=fe Pooledð Þ ð9Þ

The percent contribution is the portion of thetotal variation observed in an experiment attrib-uted to each significant factor and/or interactionwhich is reflected. The percent contribution is a

function of the sums of squares for each significantitem and it indicates the relative power of a factorand/or interactions to reduce the variation. If thefactor and/or interaction levels are controlledprecisely, the total variation could be reduced bythe amount indicated by the percent contribution.The variation due to a factor or interactioncontains some amount due to error; it is repre-sented by the following form for factor given inEquation (10).

If parameter A is significant then,

SS0A ¼ SSA � Ve � VAð Þ ð10Þ

Similarly, for other significant parameters, sumof squares is calculated. The subtracted amount ofsum of squares must be added to the error sum ofsquares given in Equation (11) for the total sumof squares to be unchanged

SS0e ¼ SSe þ Ve � VAð Þ ð11Þ

Percent contribution due to A(PA)¼(SS0A/SST)� 100 and it is similarly calculated forremaining factors.

The expected value of the sums of square (SS0)for each factor is computed using the percentcontribution (P) for each factor and is calculatedfor casting defects and S/N ratio, as shown inTables 7 and 8, respectively.

5.2.2 Estimation of mean

Once an experiment is conducted, the optimumtreatment condition within the experiment is deter-mined. There is the second situation to tackle andhence the most direct way to estimate the mean forthat treatment condition is to average all the resultsfor the trials which are set at those particular levels.The estimation of mean for casting defects isachieved by the following equation given asEquation (12), Syrcos [28].

� ¼ Tþ ½A3 �T � þ ½B1 �T � þ ½C1 � T � þ ½D1 � T �

þ ½E1 � T � þ ½F3 � T � þ ½G1 �T �

þ ½H1 �T � þ ½I1 � T �

� ¼ A3 þ B1 þC1 þD1 þ E1 þ F3 þG1

þH1 þ I1 � 8T

� ¼ 7:354þ 7:175þ 7:357þ 7:430þ 7:259þ 7:461

þ 7:435þ 7:337þ 7:361� 8� 7:464

� ¼ 6:457% ð12Þ

where � is the casting defects at the optimal castingparameters; T the average casting defects of allcontrol factors; A3 the lower value of averagecasting defect when factor A (moisture content) isat level 3; B1 the lower value of average castingdefect when factor B (permeability) is at level 1; C1


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the lower value of average casting defect whenfactor C (loss on ignition) is at level 1; D1 the lowervalue of average casting defect when factor D(compressive strength) is at level 1; E1 the lowervalue of average casting defect when factor E(volatile content) is at level 1; F3 the lower value ofaverage casting defect when factor F (vent holes) isat level 3; G1 the lower value of average castingdefect when factor G (pouring time) is at level 1; H1

the lower value of average casting defect whenfactor H (pouring temperature) is at level 1; and I1the lower value of average casting defect whenfactor I (mold pressure) is at level 1.

5.2.3 Confidence interval around the estimatedmean

The optimum value of casting defects is predictedat the selected levels of significant parameters. Theestimate of the mean (�) is only a point estimate

based on the average of the results obtained from

the experiment. Statistically, this provides a 50%

chance of the true average being greater than � and

a 50% chance of the true average being less than �.

The confidence level is the maximum and minimum

value between which the true average should fall at

some stated level. There are three different types of

confidence intervals (CIs) according to Frayce et al.

[10] and Taguchi [30], 95% CI of confirmation

experiments (CICE) and of population (CIPOP) is

calculated using the following equations given as

Equations (13) and (14):

CICE ¼ ½Fð�, 1, �eÞVe 1=�eff þ 1=r� �

�1=2

ð13Þ

CIPOP ¼ F �, 1,�eð ÞVe 1=�eff� �� 1=2

ð14Þ

where � is the level of risk, (Ve) the error variance,

(�e) the degrees of freedom for error, �eff the

Table 7. ANOVA for casting defects including percent contribution at 95% confidence limits.

SourceSum of

squares (SS)Degrees offreedom (�) Variance (V) F-ratioa Expected (SS0)

Percentcontribution (P)

A 0.3885 2 1.1942 10.497 0.3849 7.35B 1.2891 2 0.6445 34.837 1.2771 24.40C 0.1563 2 0.0781 4.2216 0.1548 2.95D 0.0266 2 0.0133 0.7189 0.0263 0.50E 0.5725 2 0.2862 15.470 0.5712 10.83F 0.0001 2 0.0000 0.0000 – –G 0.0328 2 0.0164 0.8864 0.0324 0.62H 0.2269 2 0.1134 6.1297 0.2248 4.29I 0.3346 2 0.1643 9.0432 0.3315 6.33A�B 0.5760 4 0.1440 7.7837 0.5733 10.95A�C 0.6299 4 0.4074 22.020 0.6223 11.89e(pooled) 1.0004 54 0.0185 1.0000 1.0000 19.11Total 5.2337 80 5.2337 100


Table 8. ANOVA for S/N ratio including percent contribution at 95% confidence limits.

SourceSum of

squares (SS)Degrees offreedom (�) Variance (V) F-ratioa Expected (SS0)

Percentcontribution (P)

A 0.6049 2 0.3024 16.345 0.5999 8.76B 1.7538 2 0.8769 47.400 1.7375 25.39C 0.2086 2 0.1043 5.6378 0.2066 3.02D 0.0363 2 0.0181 0.9783 0.0356 0.52E 0.6959 2 0.3479 18.805 0.6894 10.07F 0.0000 2 0.0000 0.0000 – –G 0.0408 2 0.0201 1.0864 0.0400 0.59H 0.2481 2 0.1244 6.7243 0.2465 3.60I 0.4240 2 0.2120 11.459 0.4200 6.13A�B 0.8152 4 0.4076 22.032 0.8076 11.80A�C 1.0175 4 0.5087 40.696 1.0080 14.73e(pooled) 1.0004 54 0.0185 1.0000 1.0000 14.61Total 6.8457 80 6.8457 100



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effective number of replications and r the numberof test trials.

�eff ¼ 81=1þ 18 ¼ 4:3

� ¼ 1- confidence limits 95%ð Þ ¼ 0:05

F -ratio 1, 0:05, 54ð Þ ¼ 4:05 tabulatedð Þ

CICE ¼ 4:05 � 0:0185 1=4:3þ 1=3ð Þ½ �1=2

¼ �0:2059, and CIPOP ¼ �0:1321

Predicted optimal range (for a confirmationruns of three experiments) at 95% CI is given inEquation (15):

�� CICE½ � / � / �þ CICE½ � ¼ 6:25 / 6:457 / 6:666

ð15Þ

The 95% CI of the predicted optimum castingdefect is given in Equation (16):

�� CICOP½ � / � / �þ CICOP½ � ¼ 6:32 / 6:457 / 6:59

ð16Þ

5.3 Confirmation experiments

The objective of the confirmation run is to deter-mine whether the selected control parameter valueswill produce better results than those produced inthe first part of the experiment. The confirmationexperiments are used to verify whether the factorsand levels chosen from an experiment cause aproduct or process to behave in a certain fashion.The confirming experiment is highly recommendedto verify the experimental conclusions. If theaverage of the results of the confirmation iswithin the range of the confidence limits, thesignificant factors as well as the appropriate levelsfor obtaining the desired results are properlychosen. If the average of the results of theconfirmation experiment is outside the range ofthe CI, the parameter selected levels to control theresults for a desired value are incorrect or haveexcessive measurements, confirming further

experimentation [16]. Three set of confirmationexperiments are conducted at the optimum settingsof the process parameters recommended by theinvestigation and the results are given in Table 9.

The average of response of casting defects ineach experiment is found to be 7.32% and the errorrange is 0.86. The error range is very small due tominimum confirmatory trials and it is in thepositive improvement. The selected parameters aswell as their appropriate levels are significantenough to obtain the desired result.

6. Confirmation results

Various flywheel green sand casting experimentsare conducted for this case study. ANOVA alongwith interpretation method is used to obtain thepercent contribution of each parameter and opti-mum levels of each parameter. Tables 9 and 10show the following results.

(1) The percent contribution of each parameterto the variation of casting defects andoptimum parameter.

(2) The optimum levels of various green sandcasting parameters for minimum castingdefects of flywheel green sand castings.

(3) The predicted range of optimum castingdefects is 6.325 6.4575 6.59 (At 95%confidence limits).

(4) The Sigma Level of the overall process ofthe company increased to 3.68 from 3.47.Higher the Sigma Level, better the processand lower the probability that a defect willoccur. The Taguchi method resulted in aquantum improvement in the Sigma Valueof the casting process, given in Figure 3.

7. Conclusion

Casting defects in sand casting process is analyzedby Taguchi method. From the results of the study,it is found that the application of Taguchi’s method

Table 9. Result of confirmation experiment.

Day/trial

Percentage ofcasting defects

Average percentageof casting defect Error (%)Trial 1 Trial 2

Day 1 7.21 7.11 7.16 0.7Day 2 6.95 7.62 7.29 0.8Day 3 7.31 7.43 7.37 0.9

Total average Total production – 942Defects – 69

7.32 0.86


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to the green sand casting process has the following

contributions:

(1) Improves the productivity of the castings

produced.(2) Increases stability of the casting process.

Before the application of Taguchi’s method,

the parameters of the casting process were

more arbitrary and difficult to control, and,

hence, the product quality suffered instabil-

ity problems. Taguchi’s method has yielded

optimized control factors, resulting in supe-

rior product quality and stability.(3) A higher product yield is possible because,

prior to the application of the Taguchi

method, the casting defects of the casting

process were 7.703%, and after the applica-

tion of Taguchi’s method, the casting defects

of the casting process declines to 7.32%. It

shows the reduction in average percentage of

rejection rate of sand casting process by

0.38% and process sigma level of the com-

pany has increased to 3.68 from 3.47.

Among the nine control parameters, the hier-

archy of process variables affecting casting defects

is established, and optimal casting defects in sand

casting process with a minimal number of experi-mental runs have been made and confirmed withthe verification experiment. The response table ofthe S/N ratio and the ANOVA analysis for all thenine control factors indicate a very strong influenceof B (permeability), E (volatile content), A (mois-ture content), and I (mold pressure) and moderateinfluence of H (pouring temperature), C (loss onignition), A�B (interaction of moisture contentand permeability), A�C (moisture content andloss on ignition), and no or little influence of F(vent holes), G (pouring time), and D (compressivestrength). The optimum parameters are A3, B1, C1,D1, E1, F3, G1, H1, and I1 found to yield the highestS/N ratios and minimum casting defects. At theseoptimal settings, the confirmation test has yieldedminimum casting defects within 95% CI.

Also, the experiments give a clear picture of theevery factor’s contribution to the variation in thegreen sand casting process and the quality can beimproved without additional investment. From thisanalysis, it is concluded that the quality is improvedby Taguchi’s method of parameter design at thelowest possible cost.

To optimize the set of parameters obtained andto make the analysis more precise and cost effec-tive, response surface methodology is suggested asa future scope for research.

Nomenclature

yi response value of observation in ithtest

Ve error varianceF(�,1,�e) F-ratio required at level of risk

�eff effective number of replications�T total degrees of freedomfe F-ratio for error pooledfa F-ratio for parameter A pooled

V factor variance of factorSS Sum of square

Table 10. Percent contribution and optimum parameters under economic considerations.

Parameterdesignation Parameter

Mean of castingdefects percentcontribution

S/N ratio of castingdefects percentcontribution

Optimumlevels

Optimumvalue

A Moisture content (%) 7.35 8.76 3 3.6B Permeability 24.40 25.39 1 120C Loss on ignition (%) 2.95 3.02 1 3.5D Compressive strength (kg/cm2) 0.50 0.52 1 1E Volatile content (%) 10.83 10.07 1 2.1F Vent holes (Nos) – – 3 10G Pouring time (s) 0.62 0.59 1 45H Pouring temperature (�C) 4.29 3.60 1 1400I Mold pressure (kg/cm2) 6.33 6.13 1 5

3.47

14.78

3.68

7.32

210

Sigma level Rejection %

3456789

101112131415 Before Taguchi application

After optimal setting Confirmation process

Figure 3. Performance level of casting process beforeand after Taguchi application.


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SS0 expected sum of square� level of risk�e degrees of freedom for error

SSA Sum of square for factor AV0 expected amount of variationP percent contribution� mean� degrees of freedomT average values of casting defectsN total number of experiments

Notes on contributors

A. Kumaravadivel is working as a Professor inMechanical Engineering Department, SudharsanEngineering College, Tamilnadu, India. He completedhis PG in Industrial Engineering at the Thiagarajarcollege of Engineering, Madurai Kamaraj University,Tamilnadu (India) and pursuing his Doctorate in thefield of Six Sigma at Anna University, Tamilnadu(India). He has presented many papers in national andinternational journals and conferences. His area ofinterest is quality and philosophical subjects.

U. Natarajan is working as an Assistant Professor inDepartment of Mechanical Engineering, ACCE&Tech. –Karaikudi (India). He completed his PhD at AnnaUniversity – Tamilnadu (India). After this, he went toSouth Africa for 2-year postdoc fellowship. He haspublished many papers in various reputed internationaljournals. He has guided research scholars in the area ofmicromachining and surface machining. His area ofinterest is micromachining, response surface methods,quality engineering subjects, etc.

C. Ilamparithi is working as an Assistant Professor inMechanical Engineering Department in SudharsanEngineering College. He completed his PG in IndustrialEngineering at the Sudharsan Engineering College,Pudukottai, Tamilnadu, India. He has presented manypapers in national and international conferences. Hisarea of interest is quality and philosophical subjects.

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A. Kumaravadivel*Department of Mechanical Engineering,

Sudharsan Engineering College, Pudukkottai, Tamilnadu, IndiaU. Natarajan

Department of Mechanical Engineering,ACCE&Tech., Karaikudi, Tamilnadu, India

C. IlamparithiDepartment of Mechanical Engineering,

Sudharsan Engineering College, Pudukkottai, Tamilnadu, India

S/N

* [email protected]


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