This research investigates the use of quantitative measures of performance to aid the grammatical synthesis of mechanical systems. Such performance measures enable search algorithms to be used to find designs that meet requirements and optimize performance by using automatically generated performance feedback, including behavioral simulation, as a guide. The work builds on a new type of production system, a parallel grammar for mechanical systems based on a Function-Behavior-Structure representation, to generate an extensive variety of designs. Geometric and topological constraints are used to bound the design space, termed the language of the grammar, to ensure the validity of designs generated. The winding mechanism of an electromechanical camera is examined as a case study using the behavioral modeling language Modelica. Behavioral simulations are run for parametric models generated by the parallel grammar and this data is used, in addition to geometric performance metrics, for performance evaluation of generated alternative designs. Multi-objective stochastic search, in the form of a hybrid pattern search developed as part of this research, is used to generate Pareto sets of optimally directed designs of winding mechanisms, showing the design of the camera chosen for the case study to be optimally directed with respect to the design objectives considered. The Pareto sets generated illustrate the range of simulation-driven solutions that can be generated and simulated automatically as well as their performance tradeoffs.

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