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RESEARCH ARTICLES | RISK + CRYSTAL BALL + ANALYTICS

Tolerance Analysis using Monte Carlo (Part 11 / 13) How do Monte Carlo analysis results differ from those derived via WCA or RSS methodologies? Let us return to the one-way clutch example and provide a practical comparison in terms of a non-linear response. From the previous posts, we recall that there are two system outputs of interest: stop angle and spring gap. These outputs are described mathematically with response equations, as transfer functions of the inputs.

Introduction to Monte Carlo Analysis (Part 10 / 13) 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.

Introduction to Monte Carlo Analysis (Part 10 / 13) 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.

Root Sum Squares Explained Graphically, continued (Part 9 / 13) 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?

Root Sum Squares Explained Graphically, continued (Part 9 / 13) 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?