State of the art of generative design and topology optimization and potential research needs

Abstract

Additive manufacturing allows us to build almost anything; traditional CAD however restricts us to known geometries and encourages the re-usage of previously designed objects, resulting in robust but nowhere near optimum designs. Generative design and topology optimization promise to close this chasm by introducing evolutionary algorithms and optimization on various target dimensions. The design is optimized using either 'gradient-based' programming techniques, for example the optimality criteria algorithm and the method of moving asymptotes, or 'non gradientbased' such as genetic algorithms SIMP and BESO. Topology optimization contributes in solving the basic engineering problem by finding the limited used material. The common bottlenecks of this technology, address different aspects of the structural design problem. This paper gives an overview over the current principles and approaches of topology optimization. We argue that the identification of the evolutionary probing of the design boundaries is the key missing element of current technologies. Additionally, we discuss the key limitation, i.e. its sensitivity to the spatial placement of the involved components and the configuration of their supporting structure. A case study of a ski binding, is presented in order to support the theory and tie the academic text to a realistic application of topology optimization.