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- Authors : Benita Evangelyne J , Dr. V. Jaiganesh
- Paper ID : IJERTV7IS070141
- Volume & Issue : Volume 07, Issue 07 (July 2018)
- Published (First Online): 05-01-2019
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License: This work is licensed under a Creative Commons Attribution 4.0 International License
Realizing Improvements in Quality by Applying Six Sigma Methodology A Foundry based Case Study
Realizing Improvements in Quality by Applying Six Sigma Methodology A Foundry based Case Study
Benita Evangelyne J 1
Department of Mechanical Engineering PSG College of Technology Coimbatore.
Dr. V. Jaiganesh 2
Associate Professor, Department of Mechanical Engineering
PSG College of Technology Coimbatore.
Abstract In todays ever-changing customer driven market, industries are expected to improve their products and processes to satisfy customer requirements. The Six Sigma approach has set a new paradigm for business excellence. Six Sigma as a process driven improvement methodology has been adopted successfully by many industries. In this work the DMAIC (Define-Measure- Analyse-Improve-Control) approach of six sigma has been followed to reduce the rejection rate in the green sand casting process. Response Surface Methodology has been employed to develop an empirical model that correlates the casting process variables with the desired quality characteristic. The developed model is further utilised to optimise the process parameters for minimizing the casting rejection.
Keywords Six Sigma, DMAIC, Rejection Rate, Response Surface Methodology, Analysis of Variance (ANOVA), Optimization
Nowadays, the industrial world has realized that the Six Sigma philosophy is certainly a viable solution to their shop floor problems and it has become one of the most important subjects of debate in quality management. Six Sigma is a well- structured methodology that can help a company to achieve expected goal through continuous improvement. For many companies, Sigma quality level is a measure of the process defect rate and thus can be used to measure the quality of the manufacturing process (i.e. a high Sigma level indicates that the process results in a lower defect rate, whereas a low Sigma level illustrates a higher defect rate. Reducing process variations is the core objective of Six Sigma projects, as process variations result in higher quality loss. Casting is one of the most economical routes to produce metallic components in which the liquid metal is directly poured into the mould cavity of required size and shape. The major drawback of casting processes is the hot tears, etc. Many foundries are interested to implement Six Sigma to improve the quality of their products. Indeed, the implementation of Six Sigma methodology into foundry has become globally popular. The main benefit of a Six Sigma program is the elimination of subjectivity in decision-making, by creating a system, where everyone in the organization collects, analyzes, and displays data in a consistent way. The prime focus is on minimizing the defects, developed in the cast iron differential housing cover castings manufactured by the green sand casting process. In Foundry DMAIC is implemented (Define, Measurement, Analyze, Improve, and Control) based Six Sigma approach to
optimize the green sand casting process parameters to made the process more robust to quality variations.
The increasing number of applications and of products is the best proof of the success of grey cast iron foundry. Various processes are now competing, to achieve both economically and technologically advantageous production of grey cast iron castings. Green sand casting is one of the most widely used manufacturing processes for producing a variety of parts. Till date, a quite significant amount of research and development work has been done in order to optimize the green sand casting process and improve the quality of the castings.
The conventional statistical tools available today are not adequate to be effective in analyzing the casting defects and optimize the processes to minimize the impact on cost of quality. The reason for these include: the statistical techniques assume known distributions to the unknown foundry processes, the need for specially designed experiments, the need for carrying out a very large number of experiments in view of e large number of factors, the need to carry out specially designed experiments on a limited number of castings and the need to filter the potential factors. Six Sigma heavily focuses on statistical analysis as it is data driven and is a methodical approach that drives the process improvements through statistical measurements and analyses. Reducing process variations is the core objective of Six Sigma methodology, as process variations result in higher quality loss. Therefore Six Sigma approaches for reducing the rejections in casting industry have been employed.
Implementation of six sigma: The fundamental objective of the Six Sigma methodology is the implementation of a measurement-based strategy that focuses on process and variation reduction, through application of Six Sigma projects. In one implementation model, all Six Sigma projects run through the independent organization, making it easy to measure the impact of the changes. However, this arrangement can create a "we versus them" mentality that can undermine the effectiveness of the Six Sigma initiatives. To avoid this, other model involves a more integrated approach. In this model, Six Sigma is incorporated into every employee's job and hence makes it more challenging to measure the impact of Six Sigma. It helps to create a culture in which a commitment
to quality and excellence is pervasive. Basically Six Sigma implementation fuels three engines- Process Design/Redesign, Process Management and Process Improvement
OBJECTIVES The objectives of the present work are
To reduce rejection/rework in foundry using DMAIC
-Six Sigma Methodology
To identify the defects and to analyze it causes
To optimize the process to reduce casting defects using RSM methodology.
Fig. 1 represents the Framework to reduce the defects of castings
TABLE I. Rejection data of Auto component
The present case study deals with reduction of rejection due to casting defects in a foundry industry. The company is making cast iron components and having rejection in the form of blow hole, misrun, cold shuts and sand inclusions. It is necessary to perform SIPOC analysis to have a better understanding of the process. HFB 64 LH Housing is chosen for case study because of its high rejection rate. SIPOC is created and is presented in Fig. 2, respectively.
Fig. 2. SIPOC for casting process
To understand and establish the baseline performance of the process in terms of sigma rating. Defect data has been collected shown in Table I for auto component LH Housing shown in Fig. 3. Using Pareto 80% of defective in castings (vital few) are found.
Fig. 3. Autocomponent
Fig. 4. Pareto chart
Fig. 4 shows the major contributor of defects that have taken place inside the industry. The six sigma level was calculated from the rejection report as shown in Fig. 5.
Published by : http://www.ijert.org
International Journal of Engineering Research & Technology (IJERT)
Vol. 7 Issue 07, July-2018
Visit to foundry
Identification of critical items
Formulation of objectives
Define the project scope
F Create project charter
Understand the foundry process
I N E
A S U R E
M Measure the exiting level
E Six sigma calculation
Continue the same process
S = 6
Processed to pareto
N A L Y S E
N Identification of vital elements
Y Cause and effect diagram, histogram
Determination of root cause
I M P R O V E
M Provide solutions to eliminate problems
O Analysis by DOE
C O N T R O L
Control the variables
Fix the problem
Control chart to predict
C O N T R O L
Results and Discussion
Fig. 1. Methodology
(This work is licensed under a Creative Commons Attribution 4.0 International License.)
TABLE II. Overall rejection data
Low moisture Low pouring temperature
Loose sand sticking
Improper gating system
Gating & Risering
Fig. 6. Cause and Effect diagram
Fig. 5. Six sigma calculations
With the help of the Pareto chart, factors that influenced the rejection most are identified. It is the key component of any defect reducing program. This is the stage at which new goals are set and route maps created for closing the gap between current and target performance level. The conventional quality technique like brainstorming, root cause analysis, Cause and effect diagram as shown in Fig. 6 etc. may be used for carrying out the analysis. The vital elements are analyzed from Pareto chart drawn in measure phase
and found to be
Sand Inclusion, Sand drop
From the cause and effect diagram the root cause found to be sand properties.
FMEA sheet is taken for risk prioritization and developed for sand inclusions / sand drop and shown in Table III.
TABLE III. FMEA for sand inclusion, sand drop
R P N
Sand inclusion, Sand fusion and
Water addition is
Machine setting is not done
Sand inclusions, sand drop
Not cleaning of mould using air guns
From FMEA & cause and effect diagram, it may be concluded that the most significant factors that affect casting defects are
green compression strength
mould hardness and
Conducted lab test on sand properties to make control charts to check whether the factors are under control or not. The control charts are shown in fig.7 and 8 and observed that moisture and compatibility are under control. The experiments are carried out using,
Moisture test moisture teller
Compatibility test Sand rammer
Control chart for moisture
In terms of actual factors
% of defects= +5800.72559 + 184.62405*A -2496.09863*B – 64.09819*C -2.03400*A*C + 27.60217*B*C (3)
Table IV. Process Control Limits and their levels
Design-Expert v9 software is used for analysis of the
Fig. 7. Control chart for moisture
Control chart for compatibility
measured responses and determining the mathematical models with best fits. The adequacy of the model is tested using the sequential F-test, and the analysis-of-variance (ANOVA) technique as shown in Table VI using the same software to obtain the best-fit model. L9 array is selected and the % of
defects is given as response to get the optimization values.
Mold Hardness (Nos)
Table V shows the entered response value for further results.
Fig. 8. Control chart for compatibility
The objective of improve phase is to empirically explore the solutions to eliminate these causes. Response surface methodology has been used for the design and modelling of the experiment. Response surface methodology is a collection of mathematical and statistical techniques that are useful for the modelling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize the response. In analyze phase we find that the sand properties are under control. Though the properties are under control we need to optimize the process within the range of control. To optimize the response the factors and their limits are to be fixed as shown in Table IV.
The response surface can be expressed by
= (1,2,,)+; (1)
includes effects such as measurement error of the response.
a second-order polynomial equation is used in RSM as given
= 0 + + 2 + (2)
=2 < =1 = 1;
where, parameters = 0, 1,…. , are called the regression
The final mathematical models for percentage defects, which can be used for prediction within same design space, are given as follows:
The fit summary for percentage defect suggests the 2Fl relationship. The ANOVA table of the 2Fl model with other adequacy measures like, R2, adjusted R2 and predicted R2 are given in Table VII.
The Model F-value of 25.81 implies the mdel is significant. There is only a 1.14% chance that a "Model F-Value" this large could occur due to noise.
Values of "Prob > F" less than 0.0500 indicate model terms are significant.
In this case AC, BC is significant model terms. Values greater than 0.1000 indicate the model terms are not significant.
If there are many insignificant model terms model reduction may improve your model.
TABLE V. Experimental design matrix and experimental results
TABLE VI. ANOVA for the percentage defect model
Design-ExpertÂ® Softw are
Predicted vs. Actual
Color points by value of
Response ( % of defects)
Sum of squares
Fig. 9. Plot of Predicted vs. Actual response of percentage casting defects
EFFECT OF PROCESS PARAMETERS ON THE PERCENTAGE OF DEFECTS
The effects of parameters are shown only for significant model terms. It is clear from the Fig. 10 that medium clay percentage and mold hardness provided a better quality of castings. With increase in clay percentage, the defects are also noted to be increasing and also it is clear from the Fig. 11 that medium moisture percentage and mold hardness provided a better quality of castings. With increase in moisture percentage, the defects are also noted to be increasing.
TABLE VII. Adequacy measures
X1 = A: A X2 = C: C
Actual Factor B: B = 3.30
EFFECT OF PROCESS PARAMETERS ON RESPONSE
11 A: A
Fig. 9 illustrates the relationship between the predicted and actual values of percentage casting defects. This figure also indicates that the developed model is adequate and predicted
results are in good agreement with measured data.
Fig. 10. Response surface plot showing the effect of parameter A and C on percentage of casting defects
Design-ExpertÂ® Software R1
X1 = B: B X2 = C: C
Actual Factor A: A = 13
Fig. 11. Response surface plot showing the effect of parameters B and C on percentage of casting defects
By numerical optimization, the target or the goal of the experiment can be set. Here the aim is to minimize the defective components in housing castings. From Fig. 12, it was noted that there were 4 possible solutions for minimizing the defect percentage close to zero.
Fig. 12. Optimal conditions obtained by numerical optimization
The main purpose of these confirmation experiments is to validate the setting obtained from Design expert software which is likely to achieve the defect free castings.
TABLE VIII. Optimum setting of parameters
A set of 3 confirmation experiments were conducted with the optimal settings producing 30 samples of housing castings.On inspecting the samples for defects, all the 30 castings were found to be defect free.
Hence, the parametric settings may be taken as optimal for producing large quantities of housing castings with minimal or no defects. In general, the use of higher clay content and mold hardness with moderate moisture percentage led to better castings as shown in Table VIII.
In this phase, verification was made whether the current process must be in control after the successful implementation of improvement solutions.
The main purpose of six sigma is not only making process improved but also having the optimum results sustained in long run. Hence, the standardization of the process is required. For that, proper documentation of the process and appropriate training of the people associated with the process should be conducted so that they can able to manage the process effectively. After implementing the optimization setting or revised approach, the product would meet Six-Sigma level and yield good results.
In Control Phase verification was made whether the expected improvements actually occur. The major defects namely SD and SI were analyzed and corrective and preventive action were taken and timely monitor is done for the process in order to ensure sustainability of the achieved results. In addition, the current sigma value is calculated which is found to be approximately 4.39. The rejection percentage declined to 8.73% from 9.18%, and the expected results and improvements were achieved and recommended for further approach.
RESULTS AND DISCUSSIONS
In The following are the conclusions drawn out of this study.
In this work, parametric optimization for controlling casting defects in LH housing was attempted by Box-Behnken design of experiments (DOE).
The major parameters that were responsible for producing casting defects in housing components were identified as proportions of clay, moisture and Mold hardness respectively.
Each parameter was analyzed with three different levels. Further the contribution of the parameters was analyzed using ANOVA technique to find their effects. Interaction effects between the factors were also studied. F-Test of the ANOVA revealed the significant parameters in the casting process. These parameters were noted to be more critical in producing quality cast components.
The optimized parametric setting was determined by Design expert software: Clay 13% Moisture 3.3% Mold Hardness
91 as a range of values for the input conditions that can be easily practiced by workmen in industries.
It can be concluded that the sigma value before improvement is 4.22, while it becomes 4.39 after improvement and rejection % decreases to 8.73 from 9.18.
Fig. 13. Comparison of existing and proposed process
The industries have become globally competitive in sellers market due to high demand. Hence production quantity is increased where the industries faces high rejection rate. This work presents the step-by-step application of the Six Sigma DMAIC methodology for reducing the rejection rate of casting in an Indian foundry unit. The research findings show that the
rejection rate of casting has been reduced from 9.18% to 8.73% for HFB 64 LH Housing item. It substantiated the fact that the efficiency and performance level of the sand-casting process can be improved by adopting the Six-Sigma approach. From this study, it is evident that applied tools detect a greater number of possible failure causes and thus the failures in the production process can be prevented.
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