Research on Intelligent Planning Method for Turning Machining Process Based on Knowledge Base

Against the backdrop of accelerating transformation in traditional mechanical manufacturing toward intelligent production models integrating mechanical, electronic, and information technologies, coupled with increasing demands for mass customization, conventional machining methods are proving inadequate to meet modern manufacturing requirements. To address these challenges, this study proposes a knowledge-based intelligent process planning system. First, to address the heterogeneity issues in knowledge aggregation during machining processes, a process knowledge model comprising three sub-models was designed. Using ontological analysis methods with OWL language, inter-model relationships were formally expressed, achieving structured knowledge representation. Furthermore, to meet the system’s substantial knowledge demands, a MySQL-based knowledge framework was developed, enabling distributed storage and the intelligent retrieval of process planning knowledge. Second, to overcome limitations like low openness and decision-making rigidity in traditional process planning, a hybrid reasoning mechanism was proposed: on the one hand, an instance and rule-based reasoning system ensures adaptability to parameter variations; on the other hand, Generative Adversarial Networks are introduced to transcend the completeness limitations of traditional knowledge reasoning, enabling the dynamic evolution of process knowledge. Finally, the intelligent process planning system was implemented in Python on the VSCode platform. Validation via typical turning cases demonstrates the system’s autonomous process planning and execution capabilities.