Worst-Case Tolerance Design and Quality Assurance via Genetic Algorithms

In many engineering designs, several components are often placed together in a mechanical assembly. Due to manufacturing variations, there is a tolerance associated with the nominal dimension of each component in the assembly. The goal of worst-case tolerance analysis is to determine the effect of the smallest and largest assembly dimensions on the product performance. Furthermore, to achieve product quality and robustness, designers must ensure that the product performance variation is minimal. Recently, genetic algorithms (GAs) have gained a great deal of attention in the field of tolerance design. The main strength of GAs lies in their ability to effectively perform directed random search in a large space of design solutions and produce optimum results. However, simultaneous treatment of tolerance analysis and robust design for quality assurance via genetic algorithms has been marginal. In this paper, we introduce a new method based on GAs, which addresses both the worst-case tolerance analysis of mechanical assemblies and robust design. A novel formulation based on manufacturing capability indices allows the GA to rank candidate designs based on varying the tolerances around the nominal design parameter values. Standard genetic operators are then applied to ensure that the product performance measure exhibits minimal variation from the desired target value. The computational results in the design of a clutch assembly highlight the advantages of the proposed methodology.