| In the field of mechanical engineering, synthesizing a mechanism to perform an intended task is deceptively complex. In this dissertation, a novel approach to automated mechanism synthesis is described which uses an evolutionary search algorithm and a technique called "convertible agents" to simultaneously find the most appropriate mechanism type for a given problem, while finding an optimum set of dimensions for that mechanism to realize a specified behavior. The convertible agent technique has been developed in response to the unique design challenges encountered when synthesizing a mechanism for both type and dimensionality. Several case studies are presented which demonstrate the approach's effectiveness over earlier solution strategies. In these studies, six different planar single-degree-of-freedom mechanism types are considered: a four-bar mechanism, Stephenson's six-bar-mechanisms (types I, II, and III), and Watt's six-bar-mechanisms (types I and II). The method is readily scalable to account for any number of different mechanism types and complexities. The developed convertible agent approach is well suited for evolutionary design applications outside of mechanism synthesis in which there are a small number of distinct topological design possibilities each with parametric variables to be optimized. |
