A DEVELOPMENT OF QUALITY IN CASTING BY MINIMIZING DEFECTS

International Journal of Recent Research in Civil and Mechanical Engineering (IJRRCME)
Vol. 1, Issue 1, pp: (31-36), Month: April 2014 - September 2014, Available at: www.paperpublications.org
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Paper Publications
A DEVELOPMENT OF QUALITY IN CASTING BY MINIMIZING DEFECTS
1PRASAN KINAGI, 2Dr. R.G MENCH
1,2BVB College of Engineering, Department Of Industrial and Production Engineering, Hubli
Abstract: In this paper design of experiment and FMEA techniques are combined to analyze casting defects. Attempt has been made to get optimal parameter setting for defects like cold shut and blow hole. Producing defect free casting is impossible. Defect analysis is carried using FMEA tool and Pareto analysis to know potential causes of failure and their effects along with correct actions to improve quality strength and productivity. The main objective of this paper is to optimize sand casting process parameter using DOE method through Taguchi method. Taguchi based L9 orthogonal array was used for experimental purpose and analysis was carried out using Minitab software for analysis of mean (ANOM) plot. In this paper the data collected is taken from one foundry.
Keywords: Casting, FMEA, DOE, Taguchi methodology, Pareto analysis.
1. INTRODUCTION
Casting is simply melting the metal and pouring molten metal into mold cavity, allowing the metal to solidify and then breaking up mold to remove casting. Casting as an old technique is the quickest link between engineering drawings 2nd manufacturing. It provides us with the possibility of forming wide range of shapes with wide range of materials. Casting process involves number of process parameters so it is difficult to produce defect free casting even in a controlled process, defects in casting are observed which challenges explanation about the cause of casting defects [1]. Casting defects analysis is the process of finding the potential failure cause of Occurrence of defects in the rejection of casting and taking necessary steps to reduce the defects and to improve the casting yield. [1-2]
In this paper, for casting defects analysis techniques like Pareto-chart, FMEA and DOE (Taguchi Method) are conversed in following section.
2. LITERATURE REVIEW
Now days in order to sustain in any variety of manufacturing industry we need to be competitive enough as rivals. The main thing we should concentrate on how we are going to minimize defects that occur in industry so necessary preventive actions should be taken.
Wilson et al. (1993) have defined the Root Cause Analysis as an analytical tool that can be used to perform a corrective and comprehensive, system based review of critical defects. Uday A. Dabade and Rahul C. Bhedasgaonkar [2] have put their emphasis on casting defect analysis using Design of Experiments and Computer Aided Casting Simulation Techniques. They work to analyze the sand related and method related defects in green sand casting. They applied Taguchi based orthogonal array for Experimental purpose and analysis was carried out using Minitab Software for analysis of variance and analysis of mean plot.
From literature review we came to know that application of Pareto analysis, failure mode effect analysis and design of experiment can significantly minimize the defects of manual casting operation.
3. METHODOLOGY
The study conducted in engine casing component manufacturing foundry where cold shut, blow hole, shrinkage, rib cut and sand inclusion are most important defects observed, and DOE is used for analysis of above mentioned casting defects. FMEA determines the potential failure cause of occurrence of defects in casting rejection.

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Paper Publications
FMEA is set of guidelines for identifying and prioritizing the potential failure or defects. Prioritizing the defects by finding the RPN (risk priority number).
Figure 1: Research Methodology
4. PROBLEM STATEMENT
To achieve or develop quality in casting by minimizing defects
 Every 10 casting 4-6 castings are rejected.
 Affecting defects are cold shut, blowhole and sand inclusion.
4.1 Objective
 Identification of defects in engine casing (EICHER 298) and analysis
 Identification of potential failure and causes through FMEA
 Quality improvement, including reduction of variability and improved process and product performance
5. DATA COLLECTION
Simplified data from the total rejection sheet is represented in the following table. Data shows the total production per month and the data is of three months. Rejection of total Eicher-298 is given in the following Table-1.

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Paper Publications
Table-1 Rejection data sheet
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
defects 3.60 2.20 1.70 1.13
Percent 41.7 25.5 19.7 13.1
Cum % 41.7 67.2 86.9 100.0
OTHERS
SAND
BH
CM
9
8
7
6
5
4
3
2
1
0
100
80
60
40
20
0
Percent
Cumulative Percent
1.13
1.70
2.20
3.60
Pareto Analysis
Figure 2: Pareto graph
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.
5.1 FMEA Implementation
Table-2 FMEA for EICHER 298 casting
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
The Risk Priority Number (RPN) is determined by three risk parameters which include Severity Rating (S), Occurrence
Rating (O), and Detection Rating (D). FMEA can be divided into two phases. The first phase (Measure) is to identify the
potential failure modes and to decide the value of Severity, Occurrence and Detection, as given in Table in the second
phase (Improve), the manager should make recommendations for correct actions.
With the help of the FMEA domains of possible improvement are clearly identified and recommended for further action.

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Paper Publications
6. 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 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. L9 orthogonal array was used the design factor and their levels are shown in Table-3
Table-3 Factors and their level
Sl. no
code
Factors(units)
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
Experimental layout for L9 Taguchi orthogonal array-Total 9 experiments were carried out and response were recorded out of 10 products in %. The S/N ratio for minimum defects coming under smaller-is–better type characteristic.
7. RESULT AND DISCUSSION
Analysis of experimental results was performed using Minitab software and ANOM plots obtained are given in Table and Figure respectively.
Table-4 Results of ANOM
Factors
Levels (dB)
Optimum level
L1
L2
L3
A
- 31.21
-35.48
-31.21
1&3
B
-33.66
-32.38
-31.85
3
C
-32.69
-32
-33.22
2
D
-32.38
-31.85
-33.66
2
Optimum mean =-29.02
ANOM plot in Figure 3 indicates that %rejection is minimum at first level and third level of pouring temperature (A1&A3), third level of inoculant (B3), moisture content (C2) and sand binder ratio (C2)
Figure 3-Response graph
144014101380-31-32-33-34-350.30.20.13.63.33.0-31-32-33-34-3560:1.260:160:0.9A Mean of SN ratios BCDMain Effects Plot for SN ratiosData MeansSignal-to-noise: Smaller is better

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Paper Publications
7.1 Analysis of Experiment Result
Table-5 Summary of ANOVA
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
From the results of ANOVA as shown in Table-5 it is observed that pouring temperature and inoculant have major contributions in controlling the defects. On the other hand, Sand-Binder ratio has moderate effect and the moisture content has least effect on defects.
Since the ANOVA has resulted in zero degree of freedom for error term, it is necessary to pool the factor having less influence for correct interpretation of results. After selecting the optimal level of process parameters, the final step is to predict and verify the adequacy of the model.
Experiments were conducted with optimal levels of factors obtained by Taguchi optimization. The measured values of defects under the optimal process conditions were used to determine the observed values of signal to noise ratio (ηobs).
Confirmation experiments were performed at the optimized setting of process parameters, results of which are shown in below Table-6.
In order to judge the closeness of observed value of signal to noise ratio with that of the predicted value, the variance of prediction error ( 2pred) is determined and the corresponding two-standard deviation confidence limits for the prediction error of the signal to noise ratio is calculated. From the results of conformity tests, summarized in Table7 and the Calculation is done.
It can be observed that the calculated value of prediction error is close to confidence limit, which indicates that the additive model of defects is adequate.
Table-6 Results of Confirmation experiment
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
Table-7 Confirmatory Test Result
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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Paper Publications
8. CONCLUSION
The following conclusions were drawn from the present investigation.
 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.
 By FMEA method, we find the potential failure mode and potential cause for failure of defect.
 Defects in casting are minimized with optimal level settings of process parameters.
 All factors considered contribute to the quality of performance.
 The optimized levels of selected process parameters obtained by Taguchi method pouringtemperature(1380&1440),inoculant(0.3),moisture-content(3.3),sand-binder ratio(60:1)
 The percentage contribution of error is within 10%, which indicates that, no important factors are left out from analysis.
REFERENCES
[1] H.C. Pandit, Amit Sata, 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.
[2] 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.
[3] Phadke.M.S. “Quality Engineering Using Robust Design”, Prentice Hall, International, New Jersey, 1989.
[4] Ross.P.J. “Taguchi Techniques for Quality Engineering”, McGraw Hill Inc., U.S.A, 1998.
[5] Piyush Kumar Pareek, “FMEA Implementation in a Foundry in Bangalore to Improve Quality and Reliability” International Journal of Mechanical Engineering and Robotic Research, ISSN 2278 – 0149 www.ijmerr.com Vol. 1, No. 2, July 2012- 2012 IJMERR.
[6] Prof B.R. Jadhav, Santosh J Jadhav, “Investigation and Analysis of Cold Shut Casting Defect and Defect Reduction By Using 7 Quality Control Tools”, International Journal of Advanced Engineering Research and Studies.
[7] Mr. Deepak Kumar, Optimization of sand casting parameter using Factorial Design; Volume: 3, ISSUE 1, JANUARY 2014, ISSN No 2277-8179.
[8] Achamyeleh A. Kassie, et al Minimization of Casting Defects, IOSR Journal of Engineering (IOSRJEN) e-ISSN: 2250-3021, P-ISSN: 2278-8719 Vol. 3, Issue 5 (May. 2013), ||V1 || PP 31-38.

A DEVELOPMENT OF QUALITY IN CASTING BY MINIMIZING DEFECTS

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