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MAJOR PROJECT ON
A DEVELOPMENT OF QUALITY IN CASTING BY MINIMIZING DEFECTS(A project undertaken at PRADEEP ENTERPRISES, HARIHARA)
Under the Guidance of Professor Dr. R.G.Mench
BYPRASAN KINAGI
M.Tech in Production Management
USN-2BV12MPM06
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Company profile Background Casting process Problem statement, objective, scope Literature review Methodology FMEA Data collection Experiment details Confirmatory test results Conclusion References
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Outline
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Harihar, Karnataka Foundry Family-owned
duct 30
Eicher 298, APEGL 435,400
Laxmi foundry, Velkast are other owned industry
Company Profile- Pradeep Enterprises
Place
Sector
Ownership
Employees
Products
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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
Background
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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.
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Casting Process
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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.
Problem Identification, Statement Objective
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Scope The project is limited to minimization of casting defects at pradeep
enterprises, harihara
Project significanceProducing 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
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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.
Literature Review
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Methodology
Rejection Analysis
Identification of causes by FMEA
Defects due to sand, pouring, mold
Pareto analysis
Identification of parameter, levels affecting defects
Selection of optimal levelReview checking and
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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.
FMEA
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For cold shutPoor pouring practiceLow pouring temperatureLow metal fluidity
For blow hole
Causes
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FMEA for Eicher298 casting process
DefectsPotential
failure modePotential cause for failure
S
O
DRPN
BlowholeInternal voids
with depressionMoisture left in mold and
core 7 6 6 252
Cold shutSmall shot like
sphere
Due to rapid solidification before filling up of mold 8 8 5 320
shrinkageReduction in
volumeDue to lack of riser system 4 4 4 64
Sand inclusionInclusion of sand on the edges of casted surface
Improper ramming of sand 7 5 5 175
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Month Production Total rejection
Cold metal
Blow hole
Sandinclusion
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
Data collection
Rejection data sheet
Data shows the total production per month and the data is of three months. Rejection of total Eicher-298
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Pareto Analysis
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.
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Experiment Details
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 mini mum casting defects. Optimization of these casting parameters based on 3 levels and 4 factors is adopted in this paper to mini mize the casting defects.
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Sl.
nocode Factors(unit)
Levels
1 2 3
1 APouring
temperature1380 1410 1440
2 B Inoculant 0.1 0.2 0.3
3 CMoisture
content3 3.3 3.6
4 DSand binder
ratio60:0.9 60:1 60:1.2
Factors and their level
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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
Parameter settingExperiment layout plan of L9 Taguchi orthogonal array
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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
Response Values And S/N Calculation
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S/N ratio
-32.04
-29.54
-32.04
-36.90
-35.56
-33.97
-32.04
-32.04
-29.54
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Factors
Levels (dB)Optimum
valueOptimum
levelL1 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
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ANOM Based On S/N Ratio
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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
Mean o
f SN r
atios
B
C D
Main Effects Plot for SN ratiosData Means
Signal-to-noise: Smaller is better
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FactorsDegrees of
FreedomSum of Squares
Mean
Squares
Contribution %
A2 36.46 18.23 74.27
B 2 5.19 2.59 10.57
C2 2.24 1.12 4.56
D2 5.19 2.59 10.57
Error 0 0 - -
Total 8 49.09 6.45 99.97
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ANOVA Based On S/N Ratio
Factor Pooled: B&C(Because of less contribution on total variation of S/N ratio)
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Sl.no Pouring
temperature
inoculant Moisture
content
Sand-
Binder
Ratio
DefectsIn %
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
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Results of Conformation experiment(optimal parameter setting)
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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
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Confirmatory Test Results
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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.
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Conclusion
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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.
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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.
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Future scope
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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
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References
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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 60 th
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.
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