Application of Deep Reinforcement Learning Driven Intelligent Decision System in Computer-Aided Industrial Design

Most of the existing computer-aided industrial design focuses on the functional development of industrial design, with less attention paid to the coordination of various design stages, which leads to designers being unable to fully utilize the advantages of computers, resulting in the cooperation and collaboration of various design teams still remaining in a traditional and single mode. The focus of this article was to propose a computer-aided design theoretical model for product shape design process control, study the process of product shape design, and divide it. To reduce the product design cycle and improve the quality of product design, this article provided control over the product design process, including market demand analysis in industrial design, product shape innovation, and product shape design. Based on the design concept, various parts of the product were CAD (Computer-aided Design) drawn and deformed to complete the design. The final optimization plan was generated using an intelligent decision system driven by Deep Reinforcement Learning (DRL). Market demand analysis in computer-aided industrial design took 15 days, and market demand analysis in computer-aided industrial design using deep reinforcement learning driven intelligent decision support systems took 10 days. This solution can shorten the product design cycle and optimize product design.