Unleash the power of Generative Design for lightweighting and reducing carbon footprints in Automotive Engineering

The automotive industry faces ongoing challenges in reducing vehicle mass and carbon emissions while ensuring structural integrity. Traditional design approaches often fail to address these issues comprehensively. This paper explores the application of generative design (GD) to optimize critical automotive components, specifically focusing on reducing mass and in turn carbon emissions. GD builds upon traditional topology optimization by employing iterative method using MELS approach to refine designs providing multiple alternative designs to choose from. MELS (Modified Extensible Lattice Sequence) specifically is used to equally spread out points in a space by minimizing clumps and empty spaces. This property of MELS makes lattice sequences an excellent space filling DOE scheme. GD leverages the design of experiments (DOE) to vary key design variables systematically to generate and consider many potential design concepts for a given problem. It also uses artificial intelligence (AI) to group solutions into concept families, uncovering innovative geometries that conventional methods may overlook. Current CAE challenges, such as lengthy development cycles and limited design exploration, are addressed by rapidly generating multiple solutions, which enables more efficient product development. In conclusion, GD presents a significant opportunity to come up with intelligent variations for design improvement that are otherwise difficult to see in a multi-dimensional application.