An interval robust optimization with maximum input tolerance for engineering design

Most existing robust optimization (RO) methods for engineering design consider parameter design and tolerance design as separate design phases. This study is to develop an interval RO strategy for engineering design realizing the approval maximum input tolerance with robustness guaranteed. Taking the errors of design variables into account, an interval RO model based on dimensional tolerance or geometrical tolerance is constructed to optimize the nominal values and tolerances of design variables simultaneously. In the constructed model, the interval midpoint and radius of the concern performance are used to evaluate the robustness, and the overall uncertainties of all design variables are quantified by integrating a dimensionless tolerance index. And then, through the reliability-based possibility degree of interval to cope with uncertain constraints it can be converted into a deterministic multi-objective optimization problem. Finally, the deterministic multi-objective optimization problem is treated by a developed robust multi-objective genetic algorithm. The results and the potential of the proposed model and method are illustrated by the numerical and engineering examples.