This document summarizes an analysis of blow hole defects in die casting. The objectives were to minimize defects using design of experiments. Process parameters like metal temperature, die temperature, and die holding time were identified as factors affecting blow hole defects. Experiments were conducted using a Taguchi design with three factors at three levels. Analysis of variance showed die temperature was the most significant factor. Optimization found the best parameters were a metal temperature of 700°C, die temperature of 300°C, and die holding time of 75 seconds. This was predicted to increase casting density by 4.13% and reduce blow holes.

1. BLOW HOLE DEFECT ANALYSIS IN
DIE CASTING
Presented by,
R.Ramesh
Assistant professor,
Mechanical Engineering,
Muthayammal Engineering College, Rasipuram.

2. INTRODUCTION:
Die Casting:
•Die casting is a metal casting process that is characterized by forcing molten
metal under high pressure into a mold cavity
•Gravity die casting is used to manufacture complex metal components
where there is a need for high structural integrity. In this process, liquid metal
is fed from below into the die used to form the component under a positive
pressure.

3. Casting defects analysis is the process of finding the root cause of occurrence
of defects in the rejection of casting and taking necessary steps to reduce the
defects and to improve the casting Yield.
Various optimization methods to the die casting process parameters:
The Gradient search method, the Finite element method (FEM) and taguchi
method. Techniques like cause-effect diagrams, design of experiments (DoE),
Six Sigma, casting simulation and artificial neural networks (ANN) are used
by various researchers for analysis of casting defects.

4. LITERATURE REVIEW
AUTHOR TITLE SUMMARY NAME OF THE
JOURNAL
Rajesh Rajkolhe,
J. G. Khan
Defects, Causes and
Their Remedies in
Casting Process:
A Review
This study aims to finding different defects in
casting, analysis of defect and providing their
remedies with their causes. In this paper an
attempt has been made to list different types
of casting defects and their root causes of
occurrence. This paper also aims to provide
correct guideline to quality control department
to find casting defects and will help them to
analyze defects which are not desired.
International Journal
of Research in Advent
Technology(2014)
Atul Bhujugade,
Vijay Sabnis
Minimization of
Casting Defects
Using Casting
Simulation
Technique and
Casting Defects
Analysis
Using Design of
Experiment
To perform the casting simulation technique
analysis for shrinkage porosity defect and
DOE used for sand casting Defect analysis.
Taguchi based L9 orthogonal array for the
experimental purpose and Analysis using
Minitab software for analysis of variance
Casting simulation technique analysis is
performed.
International Journal
for Research in
Applied Science &
Engineering
Technology
(IJRASET)(2015)

5. Satish Kumar,
Arun Kumar
Gupta, Pankaj
Chandna
Optimization of
Process Parameters
of Pressure
Die Casting using
Taguchi
Methodology
Pressure die casting is usually applied for
casting of aluminum alloys. The optimization of
controllable process parameters such as
solidification time, molten temperature, filling
time, injection Pressure and plunger velocity.
Moreover, by selection of optimum process
parameters the pressure die casting defects such
as porosity, insufficient spread of molten
material, flash etc. are also minimized.
International Journal of
Mechanical
Engineering (2012)
Kulkarni Sanjay
Kumar J K Sawale
, Sampath Rao
Study of effect of
process parameter
setting on porosity
levels of
aluminium pressure
die casting process
using Taguchi
Methodology
in this work that die casting parameters which
are related with machine such as first phase
speed, second phase speed, first phase length
and injection pressure all have significant
influence on porosity level. The quality
assessment of the die casting part was based on
porosity measurement. The experiment have
been performed as per the combination of levels
of different process parameters suggested by L9
orthogonal array and conformation experiments
have been performed to validate the optimum
levels of different parameters
IOSR Journal of
Mechanical and Civil
Engineering (IOSR-
JMCE) (2013)

6. Rasik A Upadhye
Dr. Ishwar P
Keswani
Optimization of
Sand Casting
Process Parameter
Using Taguchi
Method in Foundry
This paper demonstrates a robust method for
formulating a strategy to find optimum factors
of process and interactions with a small
number of experiments. The process
parameters considered are moisture, sand
particle size, green compression strength,
mould hardness, permeability, pouring
temperature, pouring time and pressure test.
The results indicated that the selected process
parameters significantly affect the casting
defects in the foundry. The improvement
expected in reduction of casting defects is
found to be 37.66 percent
International Journal
of Engineering
Research &
Technology (IJERT) ,
(2012).

7. PROBLEM FORMULATION:
During the process of die casting, there is always a chance where defects will occur. Minor
defects can be adjusted easily but high reject rates could lead to significant changes at a
high cost. Therefore it is essential for die casters to have knowledge on the types of defects
and be able to identify the exact root cause, hence eliminate the losses.
In order to determine the quality of a casting, you must be able to identify the major
defects are analyzed.
Main type of defects are
Surface defects:
Cold flow, Cold shut, Non-fill , Poor-fill , Laps , Flow lines, Mis-run
Internal defects:
Inclusions, sludge, Porosity, blowholes
Dimensional defects:
Die temperature, Die Condition, Force of injection, Ejection

8. PROBLEM DESCRIPTION
• From the literature review, I have choose the problem of blow hole defect
reduction.
• Blow hole problem is major problem of die casting Industry. So in order
to analyse and identifies the defect root cause and eliminate the major
problem.
• Due to some environmental factor suddenly affect the casting quality. So,
in order to minimize the die casting defects by using design of
experiments.

9. OBJECTIVE
To minimize the die casting defect by using
design of experiment method

10. METHODOLOGY
Collecting casting defects
from die casting
Identify the major defects
Analyzing the casting defect
Identifying the cause of defect
Minimize the defect by
optimization techniques
Results and conclusions

11. DETAILS ON THE ANALYSIS:
•In my project work, I am going to optimize the process parameters by using analytical
methods i.e.-Statistical modeling and DoE for historical data to be analyzed, from
Efficient Engineering Products, Coimbatore.
•Die casting is a complex process, which is controlled by many process parameters
such as die related parameters and machine related parameters. Which has direct
impact on casting quality, improper setting is mainly focused on same issue.
•The main issue is blow hole and porosity, which is caused by improper setting of
process parameters.
•In this paper an industrial component having blow hole problem has been taken.
•This study proposes the application of taguchi methodology in identifying the
optimum process parameters in order to improve the casting quality and reduce the
defects.

14.  From cause and effect diagaram, the major causes are
prioritized and analyse controllable factor are taken like
metal temperature, die temperature, die holding time.
This factor can be affect the die casting defect. So, proper
process parameter setting to be carried out the analysis.

15. TAGUCHI DESIGN PROCEDURE
Taguchi’s design procedure involves the
following steps.
• Determine suitable working levels of the design
• Select proper orthogonal array
•Run experiment
• Analyse the data
• Identify optimal parameter
• Conformation run
•Determine the result of parameter design

16. Factor and Levels
Number of factors = 3
Number of Levels = 3
S.N
o
FACTOR SYMBOL RANGE LEVEL
1
LEVEL
2
LEVEL
3
1 Metal melting
temperature
A 650°C - 700°C 650°C 675°C 700°C
2 Die temperature B 250°C- 300°C 250°C 275°C 300°C
3 Die holding time C 65 sec to 85 sec 65sec 75sec 85sec

17. Response
•A cause and effect diagram can be used for identifying the
parameters that affects the response.
•In this study, casting density has been chosen as a response,
since if the casting density is higher, lower the internal defects
such as blow holes and porosity.
•Density of casting
Where,
ρ –density of casting (gm/cm3)
w1- Weight of the casting in air (gram)
W2 –Weight of the casting in water (gram)
ρ water - density of water (1 gm/cm3)

18. Degree of freedom
•The degree of freedom is a very important value because it
determines the minimum number of treatment conditions
.DOF is calculated using the following formula.
•DOF = (Number of Level-1) for each factor+ (Number of
levels-1)(Number of levels-1)for each Interaction + one for
the average.
Dof = (3-1)3+1
= 7
• Then the OA 9 table was selected.

19. Experiment Number
Levels
A B C
1 1 1 1
2 1 2 2
3 1 3 3
4 2 1 2
5 2 2 3
6 2 3 1
7 3 1 3
8 3 2 1
9 3 3 2

20. EXPERIMENTAL PROCEDURE:
•The experimental setup consists of die casting cell comprising of
Gravity die casting machine, an electric holding furnace. The die
casting machine is manually operated by the use of ladler for
pouring the molten metal from the melting cum holding furnace.
•The various settings required for this experiment can be set
manually and it is monitored.
•Before starting the experiment, the dies were preheated to a
temperature of 250°C to 300°C using a gas burner.
•The die temperature was measured by using infrared gun.
•A total of 9 different combinations shown in the above Table were
tried and parts were cast for each combination, which totals of 9
casting formed.

21. Test Run Design
Trail No: Metal Temperature (A)
in °C
Die Temperature (B)
in °C
Die holding time
(C) in Seconds
1 650 250 65
2 650 275 75
3 650 300 85
4 675 250 75
5 675 275 85
6 675 300 65
7 700 250 85
8 700 275 65
9 700 300 75

22. Experimental Results:
•The density of the casting was measured by Archimedes
principle and the results are shown below.
•Minitab 16 software has been used to carry out Taguchi
optimization.
•The response value is taken as density of the sample casting that
were cast under each trail condition.
•Then S/N ratio were computed based upon the formula S/N
ratio for larger the better characteristics.
S/N ratio = -10 log 10 (1/r Σ (1/yi
2))
where,
r -number of observations,
yi -Response value for each trail.
The experimental results are shown in table 3.

23. MODEL S/N RATIO CALCULATION
S/N ratio for treatment condition 1
S/N ratio = -10log10 (Σ (1/2.3332)/1)
= 7.35829
Similarly the S/N ratio for all treatment condition were
calculated and tabulated.

24. MAIN EFFECT CALCULATION
The main effect factor A at Level 1 & 2
•Effect of factor A1 = (2.333+ 2.702 +2.627)/3
= 2.554
•Effect of factor A2 = (2.672+ 2.649 +2.743)/3
= 2.688
Similarly the main effect for all other factors at all levels
main effect calculation are carried out and is shown in
table

25. DENSITY CALCULATION:
•Weight of the casting air = 1500 g
•Weight of the casting in water = 950 g
•Density of water = 1 g/cm3
ρ = ( 1500/(1500-950))*1
= 2.727 g/cm3

26. Test Response Value
Trail A B C Density (g/cm3) S/N ratio
1 1 1 1 2.333 7.35829
2 1 2 2 2.702 8.63371
3 1 3 3 2.627 8.38920
4 2 1 2 2.672 8.53673
5 2 2 3 2.649 8.46164
6 2 3 1 2.743 8.76452
7 3 1 3 2.654 8.47802
8 3 2 1 2.690 8.59505
9 3 3 2 2.727 8.71370

27. Response Table for Signal to Noise Ratios
Larger is better
Level A B C
1 8.127 8.124 8.239
2 8.588 8.563 8.628
3 8.596 8.622 8.443
Delta 0.469 0.498 0.389
Rank 2 1 3
Response Table for Means
Level A B C
1 2.554 2.553 2.589
2 2.688 2.680 2.700
3 2.690 2.699 2.643
Delta 0.136 0.146 0.112
Rank 2 1 3

28. 321
8.70
8.55
8.40
8.25
8.10
321
321
8.70
8.55
8.40
8.25
8.10
A
MeanofSNratios
B
C
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Larger is better

29. 321
2.70
2.65
2.60
2.55
321
321
2.70
2.65
2.60
2.55
A
MeanofMeans
B
C
Main Effects Plot for Means
Data Means

30. •From the response graph the optimized results are A3, B3,
and C2. Higher the Density values to reduce the internal
defect of blow hole.
•So, the optimized process parameters are
Metal Temperature: 700°C
Die Temperature: 300°C
Die holding time: 75sec
These are values to conduct the experiment.

31. ANOVA table for Means and S/N Ratios:
Analysis of Variance for SN ratios
Source DF Seq SS Adj SS Adj MS F P
A 2 0.4317 0.4317 0.2158 1.34 0.428
B 2 0.4444 0.4444 0.2222 1.38 0.421
C 2 0.2269 0.2269 0.1134 0.70 0.587
Residual
Error 2 0.3226 0.3226 0.1613
Total 8 1.4256
Analysis of Variance for Means
Source DF Seq SS Adj SS Adj MS F P
A 2 0.03655 0.03655 0.018274 1.36 0.424
B 2 0.03788 0.03788 0.018939 1.41 0.416
C 2 0.01871 0.01871 0.009353 0.69 0.590
Residual
Error 2 0.02696 0.02696 0.013478
Total 8 0.12009

32. •From the ANOVA table for SN ratio, F value is
most significant factor of Die temperature and next
factor is Metal Temperature.
• Die holding time are also significant.

33. Prediction of optimum response:
•To predict the mean at the optimum condition and then compare them against a
confirmation experiment.
•For mean response the overall average of casting density (ρ) is 2.644.
•The predicted optimal response (μ) is:
µ predicted = Estimate of the process mean at optimum condition
= ρ.+(A3-ρ.)+(B3-ρ.)+(C2-ρ.)
Where,
µ = Predicted Value,
ρ. = Mean responses of L9 taguchi method,
A3 = Mean Metal melting temperature at higher level,
B3 = Mean Die temperature at higher level,
C2 = Mean Die holding time at middle level.
So, the prediction equation the value is
= 2.644+ (2.690-2.644)+(2.699-2.644)+(2.700-2.644)
= 2.644 +0.046+0.055+0.056
= 2.801 g/cm3

34. Percentage of Predicted increase:
= (1- (Average of response/predicted response))*100
= (1-(2.644/2.801))*100
= 5.60% of Predicted increase.
The optimum casting density of the experiment is 2.758 g/cm3.
= (1-(Average of response/Optimum response))*100
= (1-(2.644/2.758))*100
= 4.13%
Then to calculate the optimum value is 4.13% increased.

35. CONCLUSION
• The optimization procedure has been made to study the effect of die casting process
parameters on casting density.
•Generally when the casting density is higher, internal defects such as blow holes and
porosity is eliminated.
• So, the basic idea is to provide a decision tool for setting optimum parameters so that
the defects occurring in the casting is reduced.
•Taguchi method was applied for optimizing the die casting process parameters, and the
results obtained using this method was useful in eliminating the blow holes problem in
housing assembly product.
•The optimum values are Metal temperature 700°C, Die temperature 300°C, Die holding
time 75seconds.
•After that the predicted optimum response of Die casting product is nearly equal to the
Taguchi optimum response.
•The future work is to analysis different confirmation test like Radiography test,
simulation, PROCAST software, and SOLID CAST software to analyzed and reduced
the Die casting defects.

36. REFERENCES:
1. Atul A. Bhujugade, Vijay B. Sabnis , Minimization of Casting Defects Using Casting
Simulation Technique and Casting Defects Analysis Using Design of Experiment,
International Journal for Research in Applied Science & EngineeringTechnology
(IJRASET)(2015).
2. Satish Kumar, Arun Kumar Gupta, Pankaj Chandna, Optimization of Process Parameters of
Pressure Die Casting using Taguchi Methodology, International Journal of
MechanicalEngineering Vol:6, No:8, 2012.
3. Rajesh Rajkolhe, J. G. Khan Defects, Causes and Their Remedies in Casting Process: A
Review, International Journal of Research in Advent Technology(2014).
4. Kulkarni Sanjay Kumar, J K Sawale , Sampath Rao, Study of effect of process parameter
setting on porosity levels of aluminium pressure die casting process using Taguchi
Methodology, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), PP 12-17,
2013.
5. Rasik A Upadhye ,Dr. Ishwar P Keswani , Optimization of Sand Casting Process Parameter
Using Taguchi Method in Foundry , International Journal of Engineering Research &
Technology (IJERT) ,2012.
6. Mr. Siddalingswami. S. Hiremath, Dr. S. R. Dulange, Advanced Tehniques in Casting Defects
And Rejection Analysis: A Study in an Industry, International Journal of Innovations in
Engineering Research and Technology [IJIERT], : ISSN2394-3696VOLUME 2, ISSUE 9, SEP.-
2015.

37. Thank YOU