A part machining process analysis model construction method via integrating grammar and bayesian network

The new generation of intelligent manufacturing, characterized by the new generation of artificial intelligence technology, is driving product design into a new stage. As an important part of product design, process design has also received a lot of research and application for its intelligent upgrade. However, the existing unified representation methods for process knowledge mainly focus on the correlation and structuring between process elements, and their representation models are still static, unable to form dynamic reasoning, resulting in a low level of intelligence. On the other hand, methods based on deep learning belong to black boxes and cannot provide explanations for the inference process. Therefore, a part machining process analysis model construction method via integrating grammar and Bayesian network is proposed to support the unified representation of process knowledge and provide dynamic reasoning, thereby promoting the intelligence level of process design.