Engineering models can and should be used to understand the effects of variability on a design. When variability is ignored, brittle designs can result that fail in service. By contrast, robust designs function properly even when subjected to off-nominal conditions. There is a need for better analytical tools to help engineers develop robust designs.
In this paper we present a general method for developing designs that are robust to variability induced by worst-case tolerances. The method adapts nonlinear programming techniques in order to determine how a design should be modified to account for variability. We show how this can be done with second order, or even exact, worst-case tolerance models. Results are given for 13 test cases that span a variety of problems. The method enables a designer to understand and account for the effects of worst-case tolerances, making it possible to build robustness into an engineering design.