Statistical Tolerancing is the alternative to worst case. To use it, however, your manufacturing group needs to provide you with their production statistics for the appropriate machines / processes / features. It's not a minor undertaking, and as indicated above, it's even harder if you have a non-Normal distribution. For a Normal distribution, determine how many sigmas you want to control within, set the mean as the nominal for your calculations and the acceptable limits as your tolerances, and work from there.
Statistical tolerancing does not have to be put on the drawing for you to use statistics in a "typical case" rather than "worst case" scenario. You can substitute the production stats into the worst-case stack-ups to find a typical case instead. This will quiet those that doubt the "worst case" results as having "never happened, and never will".
A comment was made above about statistical tolerancing being an interesting theory and not being in the "real world". That is true for many companies that do not understand the reason for going this route. Where you don't require that
every part-A will fit with
every part-B, but that rather eventually
every part-A will mate with
some part-B, then statistical tolerancing can save you sums of money. Ever had a screw that didn't fit in the first hole that you tried, so you went and tried it in another hole where it did fit, then grabbed another screw from the same batch and fit into the first hole... that's statistical tolerancing at its simplest. Statistical processing is used in making bearings, automobiles, fasteners, electronic components,...
Jim Sykes, P.Eng, GDTP-S
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