A knowledge-guided process planning approach with reinforcement learning

Abstract

With the widespread application of computer-aided technologies such as CAD and CAM in the manufacturing industry, a growing number of process documents and design documents generate multi-source process knowledge and expert experience. However, due to the diverse and complex representation of process knowledge, there is a need for more effective methods to mine a large amount of multi-source information and to exploit the explicit and implicit relationships between the knowledge contained in process knowledge. Effective knowledge reuse in process planning still needs to be improved. This paper proposes a reinforcement learning approach that combines knowledge graphs and process decision-making activities in process planning to exploit the learning potential of process knowledge graphs. Firstly, a reinforcement learning environment for process planning is introduced to model the process planning decision-making phase as a sequential recommendation of process knowledge. Then, this paper designs in detail the state representation method that combines process decision sequences and potential relationships between processes. This paper also creates the composite reward function that combines the process planning environment. In addition, a new algorithm is proposed for learning the proposed model more efficiently. Experimental results show that the network structure has more accurate recommendation results than other methods.