1. IE7610 – FUNDAMENTALS OF SIX SIGMA
SPRING/SUMMER 2016
SIX SIGMA PROJECT REPORT
“Application of Six Sigma DMAIC methodology to sand-casting
process”
PRESENTED TO:
DR. JAYANT SINGH TREWN (MASTER BLACK BELT)
PRESENTED BY:
 PARAG KAPILE – FX7378
 TARUN VERMA – FX7504

2. SUMMARY:
Six sigma methodology is conducted in foundry located at an industrial Estate in
Southern India. This company manufactures flywheel outer casings and flywheel by
using sand casting techniques. Our aim is to minimize the defects in this process.
The tools which we would using in this process is cause and effect matrix and
DMAIC Six-Sigma methodology. In this study we will be investigating the effect of
working parameters like moisture content, green strength, permeability and loss of
ignition on sand preparation. Using DMAIC Six-Sigma methodology, the optimized
process parameters were taken for experiment and better performance is obtained
in the production process is confirmed.
The project is important for the company since it will minimize the defects in
flywheel casting as well as improve customer satisfaction. If this project is not
completed it will harm the reputation of the company as well as result in the loss
of customers, material cost and labor.
In order get the best possible solution, we have come up with a project comprises
Structured Project Guide(SPG), a voice over presentation and a detailed report to
solve the issues which we encountered in sand casting of a flywheel. Under the
guidance of Professor Jayant Trewn, we have successfully integrated SPG and
completed the project which can help the company to improvise their production,
minimize the defects and increased the customer satisfaction.

3. APPLICATION OF SIX SIGMA DMAIC METHODOLOGY TO SAND-
CASTING PROCESS
TOLLGATE 1:
DEFINE:
Define is the first process of DMAIC where the problem is stated. In this phase the
project leaders are responsible for clarifying the purpose and the scope of the
project. This phase helps us to get the basic understanding of the process to be
improved and it is used to determine the customer perception and expectation for
quality.
Business case:
A proper strategy is not being followed to control production defects and therefore
defects are occurring per production and the combined reasons for the defects to
occur are poor design, lack of knowledge in the usage of resources, ignorance of
operational instructions, poor material handling, improper planning of managing
activities, lack of training and poor employee commitment towards work.
Problem statement:
Whenever a defect occurs in castings, various departments in the foundry normally
blame each other for its occurrence due to the involvement of these departments.
Defects may occur due to single cause or a combination of causes. Correct
identification of the root causes of the defect is difficult because of the involvement
of various factors which include technical factors like process design, process flow,
pattern shop, sand preparation, core making and melting as well as human factors.
The objective of this project is to minimize the percentage of defects, rejections of
the product of a green sand casting process.

4. KPOV:
It is tool used to measure the effectiveness of the project that are designed to
improve business processes. This tool breaks down main objective(Y) into sub-
objectives (y1, y2, y3). Contradicting factors(CF) are the factors that should not get
adversely affected by achieving the project objective. Boundary condition(BC) is the
assumed level of influencing factors. They are not controlled by the project team.

5. TOLLGATE 2:
This tools includes SIPOC chart, KPOV-KPIV map. SIPOC tool briefly tells about the
inputs and outputs of one or more processes in tabular form. Customer of a process
step is the process owner of the next step. Output of the process step is the input
of the next step. Inputs can be added to each step from an external supplier or from
previous step. Input from the previous step is supplied by the process owner.

6. TOLLGATE 3:
MEASURE:
in this phase various data is collected. Process Management Chart (PMC) helps us
to determine what sort of data is required, which specific team member is
responsible for collecting the data and at what frequency the data is being
collected.
In this phase Baseline sigma is calculated using the below formulae:

7.  Total opportunities = units * opportunities
 DPO = Number of defects/ total opportunities.
 DPMO = DPO* 1,000,000
DEFECT:
DEFECTS COUNT
Crack 85
Shrinkage 135
Time of Cycle 87
Blow holes 185
Dirt 66
TOLLGATE 5:
ANALYZE:
It is the initial phase of the statistical analysis of the problem. In this phase we
statically review the different families of the variation to determine which are
significant contributors to the output. It is done with the beginning with the theory
null hypothesis. The analysis will fail to reject or reject the theory.

8. POSSIBLE CAUSE LIST:
We will implement statistical tools in order so that to find the problems and
opportunities to improve the process. Here we are using statistical tool called
Pareto Chart.

9. PARETO CHART:
It is a type of chart which contains both bars and line graphs where the individual
values are represented in descending order by bars and cumulative total is
represented by the line.
FISHBONE DIAGRAM:
It is also called as cause and effect diagram. It identifies many possible causes for
an effect or a problem. It helps to sort ideas into useful categories.

10. TOLLGATE 6 – 7:
IMPROVE:
In this phase, all the team effort is put down to action. The solutions are identified
and implement to reduce variation and improve target performance.
We have jot down the required solutions to improve the current running process.
The table given below shows the possible solution to be implemented.

11. RESULTS:
Optimal conditions predicted were verified experimentally and compared with the
calculated data to validate the fitness of the model.
Above figure shows the validation of experimental results of the casting results.

12. The above figure shows the comparison between the existing and proposed
process. The existing process implemented shows greater number of KPIV.

13. The above figure shows comparison between existing and proposed process. The
costs are greater in the proposed than in the existing process and it can be justified
with the profit being greater under the proposed process.
The above figure shows comparison for sigma level and rejection between the
existing and proposed process. The scrap rework for two months’ flywheel casting
production declined while the sigma level of the company increased to higher level.
CONCLUSION:
Hence we have successfully improved the efficiency and performance level of the
sand casting process by using DMAIC Methodology of Six Sigma.

14. REFERENCES:
 Pavletic D, Fakin S, Sokovic M (2004) Six Sigma in process design, StrojVestn.
J Mech Eng 50(3):157–167
 Sahoo AK, Tiwarib MK, Milehamc AR (2008) Six Sigma based approach to
optimize radial forging operation variables. J Mater Process Technol
202:125–136
 SchonK(2006)WaysofimplementingSixSigmainanon-American culture. Int J
Six Sigma Compet Advantage 2(4):404–428