Depending upon how complex or simplistic your tolerance stack-up is for your system, this method may be excessive. The more sampling that is done, the more accurate the distribution information will be for decisions. The underlying principle uses randomness to solve the tolerance problem and generates a probability distributionof outcomes. This method uses an algorithmthat relies on a repeated experiment usingrandom sampling to obtain numerical results. Using the RMSE approach, the statistical worst case would be sqrt(56) = 7.48.Ī more sophisticated model comprises a compute-intensive Monte Carlo simulation. For example, using the min/max stack-up of tolerances 2, 4, 6 would compute to a total error of 12. In Excel, the analysis for a three-component tolerance error (Ern) stack-up contribution would look something like this: =SQRT(SUMSQ(ErA + ErB + ErB)). Then, the square root of this value is taken for the statistical worst-case tolerance. The RMSE model comprises the sum of the squares of all the worst-case tolerances. While possible, it is statistically unlikely that the worst-case tolerance conditions will occur on multiple components from different lots of materials from different vendors.Ī more balanced approach is to use a general statistical tolerance analysis by means of a root mean square error (RMSE) comparison. However, in most cases, this can be too conservative. A quick worst-case tolerance check against a summation of absolute minimum and maximum tolerances can be a starting point. Tolerance Analysisįor a tolerance analysis, identify stack-up conditions where multiple component variances sum together for a violation of system limits. But, a manufacturing line-down situation could also result if a tolerance stack-up problem is not detected until components are in the process of assembly. If it is with the end-customer, it is of course a non-starter.

If any can be found, then determine where the issue would first be identified.

Identify the tolerance requirement limits where manufacturing or performance of the final system are adversely impacted. A lower-cost component may come with the unwanted side-effect of wider tolerances and less manufacturing control. Mathematically test these against the requirements of the design to see if there will be violations within the system.Ĭonsider the impact of the total variance against the cost and performance during the design stage. Determine the expected tolerance variance from suppliers and vendors during the design stage. The expected attribute variance over the manufacturing life must also be part of the decision process for their design-in use. Components and part selection should not be chosen based only on functional use, cost, and performance within the design. During the product design stage, a tolerance stack-up review should be part of any robust development.

Tolerance stack up