Conceptual design is a vital part of the design process during which designers first envision new ideas and then synthesize them into physical configurations that meet certain design specifications. In this research, a suite of computational tools is developed that assists the designers in performing this nontrivial task of navigating the design space for creating conceptual design solutions. The methodology is based on automating the function-based synthesis paradigm by combining various computational methods. Accordingly, three nested search algorithms are developed and integrated to capture different design decisions at various stages of conceptual design. The implemented system provides a method for automatically generating novel alternative solutions to real design problems. The application of the approach to the design of an electromechanical device shows the method's range of capabilities and how it serves as a comparison to human conceptual design generation and as a tool suite to complement the skills of a designer.

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A method for automating the design of simple and compound gear trains using graph grammars is described. The resulting computational tool removes the tedium for engineering designers searching through the immense number of possible gear choices and combinations by hand. The variables that are automatically optimized by the computational tool include the gear dimensions as well as the location of the gears in space. The gear trains are optimized using a three-step process. The first step is a tree-search based on a language of gear rules which represent all possible gear train configurations. The second step optimizes the discrete values such as number of teeth through an exhaustive search of a gear catalog. The final step is a gradient-based algorithm which optimizes the non-discrete variables such as angles and lengths in the positioning of the gears. The advantage of this method is that the graph grammar allows all possible simple and compound gear trains to be included in the search space while the method of optimization ensures the optimal candidate for a given problem is chosen with the tuned parameters.