A deep reinforcement learning approach for the multi-objective, segment-based generative design of sheet metal components

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.