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Monte Carlo Simulation

Monte Carlo Simulation

Monte Carlo Simulation

(OP)
Hi,

I need some help. I'm trying to run a true position tolerance analysis using the Monte Carlo method. I have googled it and haven't found much help. can anyone recommend or have some useful information on how to do one of these simulations using excel?

RE: Monte Carlo Simulation

Sure . Monte carlo means that instead of using a structured approach for selecting the settings for factors in a multirun experiment, the factor setting is random in each run.

this has the advantage that if one setting for one factor causes a failed run, at least it is likely to be just one run, instead of several in structured experiment.

Cheers

Greg Locock


New here? Try reading these, they might help FAQ731-376: Eng-Tips.com Forum Policies http://eng-tips.com/market.cfm?

RE: Monte Carlo Simulation

Disadvantages

- tends to excessively explore the near centre region, which is often not very interesting

-needs more runs than say D optimal

-statistically it is not as neat - analysis involves a PCA type approach instead of just adding and subtracting columns

advantages

- if you decide to curtail, or expand, the number of runs then it is still meaningful, whereas in a taguchi style designed experiment it is difficult (but not impossible) to make use of runs from a smaller experiment.

-less likely to wipe out many runs due to one bad setting for one factor.

Cheers

Greg Locock


New here? Try reading these, they might help FAQ731-376: Eng-Tips.com Forum Policies http://eng-tips.com/market.cfm?

RE: Monte Carlo Simulation

You can use Excel to run a Monte Carlo simulation if you have some existing manufacturing data. The inputs would be things like the mean and the standard deviation.

The Excel funtion to use is typically NORMINV(probability, mean, standard_dev). This returns the inverse of the normal cumulative distribution for a specified mean and standard deviation, along with a weight factor (the probability). If you use RAND() in place of probability, and use the expected process capability to predict the standard_dev (such as the total tolerance divided by 3, assuming a 3-sigma process), you can create a series of cells corresponding to each dimension or variable in your stack. Then you have the computer run thousands of trials and show the overall average and standard deviation.

A monkey wrench in your scenario will be if the position tolerance has any modifiers such as "M" -- the extra bonus or shift tolerance can be accounted for, but it gets a little tricky.

John-Paul Belanger
Certified Sr. GD&T Professional
Geometric Learning Systems
http://www.gdtseminars.com

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