G-DfAM: a methodological proposal of generative design for additive manufacturing in the automotive industry

Abstract

Metal additive manufacturing is an emerging technology in the industry and has a great potential. Moreover, new technologies like generative design can maximize this potential by computing complex optimized parts for additive manufacturing solutions. However, there is a lack of methodologies combining both recent and promising technologies. This paper first establishes a state of the art of design for additive manufacturing (DfAM) and generative design methodologies. Then it proposes a generic workflow for generative design tools and it proposes a challenge approach to develop a new DfAM method including generative design tools. Finally, a global 4-step methodology maximizing the potential of generative design and additive manufacturing, the G-DfAM method, is presented and validated through an automotive use case.