RESEARCH ARTICLES | RISK + CRYSTAL BALL + ANALYTICS

In past blogs, I have waxed eloquent about two traditional methods of performing Tolerance Analysis, the Worst Case Analysis and the Root Sum Squares. With the advent of ever-more-powerful processors and the increasing importance engineering organizations place on transfer functions, the next logical step is to use these resources and predict system variation with Monte Carlo Analysis.

In past blogs, I have waxed eloquent about two traditional methods of performing Tolerance Analysis, the Worst Case Analysis and the Root Sum Squares. With the advent of ever-more-powerful processors and the increasing importance engineering organizations place on transfer functions, the next logical step is to use these resources and predict system variation with Monte Carlo Analysis.

The other RSS equation, that of predicted output mean, has a term dependent on 2nd derivatives that is initially non-intuitive:

 

Why is that second term there?

 

The other RSS equation, that of predicted output mean, has a term dependent on 2nd derivatives that is initially non-intuitive:

 

Why is that second term there?

 

A few posts ago, I explained the nature of transfer functions and response surfaces and how they impact variational studies when non-linearities are concerned. Now that we have the context of the RSS equations in hand, let us examine the behavior of transfer functions more thoroughly.

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