Design For Six Sigma (DFSS) Example For Pump Design and Tolerance

P

processanalyzer

#1
This presentation is an example of DFSS (Design For Six Sigma) where the main goal is how to identify, assess and reduce variation during the design process.

This simple example deals with the design of a pump to Six Sigma performance showing the application of robust design concepts, Monte Carlo simulation and tolerance design. In line with the DFSS methodology it is possible to predict (under certain assumption like the knowledge of the transfer function Y = f(X)) the defect rate of a subsystem in the early phases of the design process limiting the number of physical prototypes.

The design process is in three steps:

Solution 1: a set of nominal values for control factors are defined together with their low cost variation (Cpk = 0.79)

Solution 2: without tightening tolerances on the control factors, we adjust only their nominal values (Cpk = 1.30)

Solution 3: we reduce the output variation acting only on the factor that has more influence on the total variation (RSS law)
The analysis was completed with the help of Minitab.


I found this interesting link, Download: Design For Six Sigma (DFSS) Example For Pump Design and Tolerance I hope this helps some one.
 

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antoine.dias

Quite Involved in Discussions
#2
Very nice example of a technique which is not used as often as it should be.

Thanks for sharing.

Best regards,

Antoine
 
S

supreecha

#3
Doing Monte Carlo Simulation in Minitab Statistical Software
By using Minitab?s ability to easily create random data, you can use Monte Carlo simulation to:
? Simulate the range of possible outcomes to aid in decision-making
? Forecast financial results or estimate project timelines
? Understand the variability in a process or system
? Find problems within a process or system
Manage risk by understanding cost/benefit relationships

Steps in the Monte Carlo Approach
1. Identify the Transfer Equation
2. Define the Input Parameters
3. Create Random Data (create a very large, random data =100,000)
4. Simulate and Analyze Process Output

Monte Carlo Using a Known Engineering Formula
A manufacturing company needs to evaluate the design of a proposed product: a small piston pump that must pump 12 ml of fluid per minute. You want to estimate the probable performance over thousands of pumps, given natural variation in
-piston diameter (D),
- stroke length (L), and
- strokes per minute (RPM).

Ideally, the pump flow across thousands of pumps will have a standard deviation no greater than 0.2 ml.

Step 1: Identify the Transfer Equation
formula that measures pump flow:
Flow (in ml) = π(D/2)2 L RPM

Step 2: Define the Input Parameters
equation: π(D/2)2 * L
Given D = 0.8 and L = 2.5, each stroke displaces 1.256 ml. So to achieve a flow of 12 ml/minute the RPM is 9.549
- diameter is normally,mean = 0.8 cm and a standard deviation =0.003 cm
- length is normally ,mean = 2.5 cm and a standard deviation = 0.15 cm
- strokes per minute is normally , mean = 9.549 RPM and a standard deviation = 0.17 RPM


Step 3: Create Random Data100,000 rows of simulated data. Starting with the simulated piston diameter data, choose Calc > Random Data > Normal

Step 4: Simulate and Analyze Process Output Go to Calc > Calculator, and set up the flow equation
Select Stat > Basic Statistics > Graphical Summary and select the Flow column.

The graphical summary of your Monte Carlo simulation output will look like as below:

 

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