Toward Cognition-Decision Bimodal Synergy Driven Generative Design for Complex Products: A Reinforcement Learning Intelligent Paradigm

Under the trend of human-centered intelligent manufacturing, the design of complex products is confronted not only with engineering challenges related to multidisciplinary integration, data complexity, and evolving requirements but also with the technical challenges arising from the paradigm shift in design processes due to autonomous design intelligence driven by AI technology. To investigate the emerging dimensions of human-centered design productivity, this paper proposes a cognition-decision bimodal synergy framework based on reinforcement learning for the generative design of complex products. This framework enables the automatic generation and optimization of design solutions by integrating design cognition and decision support knowledge into a unified knowledge triplet, which facilitates dynamic interactions between the agent and the environment. The framework consists of three primary components: cognitive and decision representations, state modeling of design solutions, and reinforcement learning model construction. To validate the feasibility and generalizability of the proposed framework, this study presents case studies involving architectural design, parametric design, and process design in the contexts of launch vehicles, autonomous mobile robots, and diesel engines. The proposed framework offers valuable insights for the future paradigm of intelligent complex product design and holds significant theoretical and practical implications for advancing the intelligent development of complex product design.