A genetic algorithm based solution to optimum tolerance synthesis of mechanical assemblies with alternate manufacturing processes—benchmarking with the exhaustive search method using the Lagrange multiplier

Abstract

Product development process involves tolerance specification on the individual component dimensions. The impact of tolerance specification on manufacturing cost has drawn the attention of product designers towards the economical tolerance synthesis using various optimization techniques. Simultaneous selection of manufacturing processes (machines) from among the alternatives for producing a toleranced feature has also been considered. The solution surface for such a problem becomes a combinatorial and multimodal function involving several local minima. In this paper a genetic algorithm based solution to this advanced tolerancing problem has been reported. The proposed algorithm is quite simple and straightforward and automatically takes care of the process selection constraints. The application of the new approach has been demonstrated with the help of a few case studies involving both linear and non-linear assemblies. Results obtained using this approach are almost similar to those obtained in closed form using the exhaustive search method based on the Lagrange multiplier. The study establishes the fact that the genetic algorithm is a promising optimization tool, which can be applied to complex tolerance synthesis problems, such as those involving interrelated dimension chains, complex cost functions and complex stack-up conditions, where traditional optimization techniques are not applicable.

Keywords

tolerance synthesis alternative manufacturing processes genetic algorithm exhaustive search based on the Lagrange multiplier set-up reduction constraints

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