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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

BVB COLLEGE OF ENGINEERING & TECHNOLOGY

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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


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

improvementBVB COLLEGE OF ENGINEERING &

TECHNOLOGY

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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


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


ANOM Based On S/N Ratio

21BVB COLLEGE OF ENGINEERING & TECHNOLOGY

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


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


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


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.


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.


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


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.


30BVB COLLEGE OF ENGINEERING & TECHNOLOGY

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