Constraint-Based Methods for Automated Computational Design Synthesis of Solution Spaces

Computers have the capability to support human designers in a variety of tasks. This includes not only releasing the human designer from routine tasks by design automation but also sparking and supporting innovation and creativity in development processes. In order to support the concept phase effectively, a wide range of possible concepts, which are quantitatively evaluated, should be considered to enable designers to explore the solution space and to make advantageous decisions towards concepts to be considered in consecutive development phases. To enable an automated systematic solution space exploration and evaluation, this research presents different approaches based on a graphbased object-oriented knowledge representation. This representation is combined with first-order logic and Boolean satisfiability as foundation for a generic automated approach for requirement-driven computational design synthesis of solution spaces. To enable the evaluation of the generated solution spaces, a generic approach to automatically translate the generated graph-based product concepts into Bond graph-based simulation models is described. Finally, a method is presented to parametrically optimize the generated concepts using simulated annealing. Here, parameterizations are generated by automatically setting up and solving constraint satisfaction problems and evaluated using the generated simulation models. The methods are validated on the case studies of chemical process engineering, automotive powertrains and 3D-Printer kinematic mechanisms. The main contributions of this research are a continuous and generic approach starting with task definitions and ending with a valid, parameterized product concept, a method which is able to determine if an engineering task is solvable for a given set of synthesis building blocks, and an approach for a generic transformation of the generated product concepts to Bond graph-based simulation models. Thus, this research provides new knowledge in terms of generic transformations between different knowledge representations in order to generate, explore and evaluate large solution spaces with an, until now, unreached expressiveness.