PROBABILISTIC DESIGN

PROBABILISTIC DESIGN

INTRODUCTION

In the design of a product for mass-production we are faced with the challenge that every item produced will be different. These differences will be slight to the casual observer, but may combine in the individual items to give vastly different performance characteristics, and thus impact the perceived quality of the product. These differences are caused by, among many other things, drift in machine settings, batch variability in material properties and operator input.

The value of each design parameter embodied in any item is therefore likely to be different from the value in any other item. If we measure the values of a design parameter (a length, say) in all the items in a production run we will get data on the frequency of occurrence of the values of the parameter. If there are sufficient data values we can rescale the frequency to give a probability. Design parameters may thus be viewed as random variables.

Most physical variables used in engineering design are in fact random variables. Standard calculations are really calculations with their mean values. If we are interested in the possible range of values our result might have, then we must use more information in our calculation algorithm than the mean values alone.

The classical approach to design is to apply safety factors to each design parameter to allow for uncertainties. If the design is complex, these safety factors can compound to cause over design with an uncertain reliability. And in some important cases, where there is an upper and lower specification or functional limit, the safety factor method cannot be used at all.

Probabilistic design studies how to make calculations with the probability distributions of the design parameters, instead of the nominal or mean values only. This will then allow the designer to design for a specific reliability or specification conformance, and hence maximize safety, quality and economy.

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