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Wittig Adão, Christoph; Muralitharan, Saruka; Li, Jiahang; Döllken, Markus; Matthiesen, Sven

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Abstract:

Current approaches for the generative design of sheet metal parts only take singular optimization goals into account. This paper presents a concept for a deep reinforcement learning approach to train an agent to generate sheet metal parts by combining segments from a predefined library. Through a weighted reward function, agents can be trained for different or combined optimization goals, such as weight, cost, or sustainability. The resulting agents enable the creation of a pareto front of optimal solutions, supporting efficient exploration of the design space for diverse design objectives.

Keywords:

design automation, computational design methods, design space exploration, artificial intelligence (AI), data-driven design