DEVELOPMENT OF QUALITY IN CASTING BY MINIMIZING DFECTS

BY
PRASAN KINAGI
M.Tech in Production Management
USN-2BV12MPM06
BVB COLLEGE OF ENGINEERING & TECHNOLOGY 1

 Company profile
 Background
 Casting process
 Problem statement, objective, scope
 Literature review
 Methodology
 FMEA
 Data collection
 Experiment details
 Confirmatory test results
 Conclusion
 References

Harihar, Karnataka
Foundry
Family-owned
duct 30
Eicher 298, APEGL 435,400
Laxmi foundry, Velkast are other owned industry
Place
Sector
Ownership
Employees
Products
BVB COLLEGE OF ENGINEERING & TECHNOLOGY
3

 Sand Casting is simply melting the metal and pouring molten metal
into mold cavity, allowing the metal to solidify and then breaking up
the mold to remove casting.
 It is also called as sand molded casting, is a metal casting process
characterized by using sand as the mold material.
 Sand casting is relatively cheap
 All foundry processes generate a certain level of rejection is closely
related to the type of casting, the processes used and the equipment's
available
 The rejected casting can only be re-melted and the value addition
made during process such as melting
4

 Most of the foundries have no precise knowledge of the main causes
of rejection because they fail to maintain a satisfactory quality
control system.
 Producing defect free casting is impossible, hence it deals with
systematic to understanding and development of quality in cast iron
foundry.
5

6

Problem Statement
 Minimizing casting defects
Project Objective
 Identification of defects in engine casing(Eicher-298) and causes
 The main objective of this project is to optimize sand casting
process parameter using DOE method through Taguchi method.

Scope
 The project is limited to minimization of casting defects at pradeep
enterprises, harihara
Project significance
Producing defect free casting is impossible, so attempt has been made
to minimize the casting defects. In today’s competitive and challenging
environment, organizations strive to have competitive advantage so
 Improve production and quality
 Increase customer satisfaction
8

 It is important for manufacturing industry to achieve quality level[1]
 Quality can be achieved by reducing defects and by controlling process
parameters relating to the potential cause of defects
 Studying the cause of defect is best solution to get rid of defects[4]
 No of defects increases due to pouring temperature, improper pouring and
lack of trained workers
 Defects motivated researcher to identify defects and to know potential
causes of failure
 From literature review it concluded that application of Pareto analysis;
failure mode effect analysis and design of experiment can significantly
minimize the defects of manual casting operation.
9

Rejection Analysis
Identification of causes
by FMEA
Defects due to sand,
pouring, mold
Pareto analysis
Identification of
parameter, levels
affecting defects
Selection of optimal level
Review checking and
improvement
10

 FMEA analysis are used to determine which failures are likely to
appear and what corrective actions are necessary for failure
prevention.
 FMEA is the set of guidelines for identifying and prioritizing the
potential failure or defects
 We are going to prioritize the defects by finding the RPN no.
11

For cold shut
Poor pouring practice
Low pouring temperature
Low metal fluidity
For blow hole
12

Defects
Potential failure
mode
Potential cause for failure
S O D
RPN
Blowhole
Internal voids
with depression
Moisture left in mold and
core
7 6 6 252
Cold shut
Small shot like
sphere
Due to rapid solidification
before filling up of mold
8 8 5 320
shrinkage
Reduction in
volume
Due to lack of riser system 4 4 4 64
Sand inclusion
Inclusion of sand
on the edges of
casted surface
Improper ramming of sand 7 5 5 175
13

Month Production Total
rejection
Cold
metal
Blow
hole
Sand
inclusion
Others
February 4210 508 202 138 96 72
March 3975 318 119 92 63 44
April 3508 186 97 30 42 17
Total 11693 1012 418 260 201 133
Rejection data sheet
Data shows the total production per month and
the data is of three months. Rejection of total
Eicher-298
14

With the help of Pareto chart, factors that influenced the rejection
most are identified. The above graph shows the percentage rejection
of defects and also cumulative percentage of defects. From the above
Pareto graph 3.6% cold metal defects, 2.2% blowhole defects occur
and it came it out as major defects and sand inclusion also effects.
15

 Experiments were carried out in foundry industry producing CI
components. The analysis of casting defects involves selecting
parameters and their levels. Performing experiments as per DOE
(Taguchi method) and collect data.
 As per rejection analysis it was found that the component EICHER-
298 rejection was maximum due to sand inclusion, blowhole and
cold shut. So this component is selected for analysis by DOE
method.
 Proper selection of the casting parameters can results in minimum
casting defects. Optimization of these casting parameters based on 3
levels and 4 factors is adopted in this paper to minimize the casting
defects.

Sl.
no
code Factors(unit)
Levels
1 2 3
1 A
Pouring
temperature
1380 1410 1440
2 B Inoculant 0.1 0.2 0.3
3 C
Moisture
content
3 3.3 3.6
4 D
Sand binder
ratio
60:0.9 60:1 60:1.2
Factors and their level

Trial No.
Level of factor
A B C D
1. 1380 0.1 3 60:0.9
2. 1380 0.2 3.3 60:1
3. 1380 0.3 3.6 60:1.2
4. 1410 0.1 3.3 60:1.2
5. 1410 0.2 3.6 60:0.9
6. 1410 0.3 3 60:1
7. 1440 0.1 3.6 60:1
8. 1440 0.2 3 60:1.2
9. 1440 0.3 3.3 60:0.9
Experiment layout plan of L9 Taguchi orthogonal array
18

Trial
No.
Level of factor
Number of defects in %
(for each 10 products)
A B C D
1. 1380 0.1 3 60:0.9 40
2. 1380 0.2 3.3 60:1 30
3. 1380 0.3 3.6 60:1.2 40
4. 1410 0.1 3.3 60:1.2 70
5. 1410 0.2 3.6 60:0.9 60
6. 1410 0.3 3 60:1 50
7. 1440 0.1 3.6 60:1 40
8. 1440 0.2 3 60:1.2 40
9. 1440 0.3 3.3 60:0.9 30
S/N ratio
-32.04
-29.54
-32.04
-36.90
-35.56
-33.97
-32.04
-32.04
-29.54
19

Factors
Levels (dB)
Optimum
value
Optimum
level
L1 L2 L3
A -31.21 -35.48 -31.21 -31.21 1&3
B -33.66 -32.38 -31.85 -31.85 3
C -32.69 -32 -33.22 -32 2
D -32.38 -31.85 -33.66 -31.85 2
Optimum mean =-29.02
20

144014101380
-31
-32
-33
-34
-35
0.30.20.1
3.63.33.0
-31
-32
-33
-34
-35
60:1.260:160:0.9
A
MeanofSNratios
B
C D
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Smaller is better
21

Factors Degrees of Freedom
Sum of
Squares
Mean
Squares
Contribution
%
A 2 36.46 18.23 74.27
B 2 5.19 2.59 10.57
C 2 2.24 1.12 4.56
D 2 5.19 2.59 10.57
Error 0 0 - -
Total 8 49.09 6.45 99.97
Factor Pooled: B&C(Because of less contribution on total variation of S/N ratio)
22

Sl.no Pouring
temperature
inoculant Moisture
content
Sand-
Binder
Ratio
Defects
In %
S/N
Ratio
1. 1380 0.3 3.3 60:0.1 20 -26.02
2. 1440 0.3 3.3 60:0.1 20
-26.02
Average signal to noise ratio= -26.02
23

Levels (A, B, C, D) 1-3-2-2
Signal to noise ratio observed (ηobs), dB -26.02
Predicted Signal to noise ratio (ηpred), dB -29.65
Prediction error, dB 3.63
Confidence limit ( 2σ), dB ±2.39
24

 Pareto principle is used to identify and evaluate different defects and
causes for these defects responsible for rejection of components at different
stages of manual metal casting operations. The correct identification of the
casting defect at initial stage is very useful for taking remedial actions.
 The optimized levels of selected process parameter so obtained by
Taguchi-method are pouring temperature(A): 1380&1440,
inoculant(B):0.3, moisture content(C): 3.3, sand binder ratio(D):60:1.
 The percentage contribution of error is within 15%, which indicates that,
no important factors are left out from analysis.
 The result of verification experiments has shown the quality of
additive model is adequate and percentage of improvement is
satisfactory.
25

 Design of experiments method such as Taguchi method can be
efficiently applied for deciding the optimum settings of process
parameters to have minimum rejection due to defects for a new
casting as well as for analysis of defects in existing casting.
26

 The future scope of this experiment can further guide in selecting
the various combinations for the process with mode trails can help
in minimizing the defects of castings.
27

1]Shepherd, N., Economics of quality and activity based management:
The bridge to continuous improvement,The Management Accounting
Magazine, Vol. 69, No. 2, pp. 29-32, 1995.
2] Schonberger, R.J., E.M. Jr, Operations Management, Fourth edition,
IL and Boston, 1991.
3] Schneider man, A.M., Optimum quality costs and zero defects: are
they contradictory concepts Quality Progress, Vol. 19, No. 11, pp. 29,
1986.
4] Achamyeleh A. Kassie, Samuel B. Assfaw,C Minimization of
casting, School of Mechanical and Industrial Engineering Bahirdar
University, Bahirdar, Ethiopia
28

5]S. Kuyucak, “Sponsored Research: Clean Steel Casting production – Evaluation of
Laboratory Castings”, American Foundry Society, 2007
6]Piyush Kumar Pareek,Fmea Implementation In A Foundry In Bangalore To Improve
Quality And Reliability
7] H.C. Pandit, AmitSata, V. V. Mane, Uday A. Dabade,2012, “A Novel Web-based
system for Casting Defect Analysis”, paper published in technical transactions of 60th
Indian Foundry Congress, 2-4th March2012, Bangalore,pp 535-544
8]Rahul Bhedasgaonkar, Uday A. Dabade, May 2012, “Analysis of casting Defects by
Design of Experiments Method”, Proceedings of 27th National Convention of
Production Engineers and National Seminar on Advancements in Manufacturing
VISION 2020,organised by BIT, Mesra, Ranchi, India.
29

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