A review of intelligent decision-making and planning methods for machining process routes

Liguo Chen, Lin Zheng, Mengqi Zhu

Intelligent decision-making and planning for machining process routes play a critical role in bridging product design and manufacturing, with direct implications for machining efficiency, production cost, and product quality. However, traditional process planning relies heavily on manual expertise and is increasingly unable to meet the demands of multi-variety, small-batch, and customized production in intelligent manufacturing. This review examines the main methodological paradigms in intelligent machining process planning, including knowledge-driven, algorithm-optimization-based, data-driven, and hybrid approaches, and discusses their principles, strengths, limitations, and application scenarios. It also reviews the roles of enabling technologies such as model-based definition, knowledge graphs, and digital twins in supporting process knowledge organization, route generation, and dynamic adaptation. On this basis, the current challenges of intelligent machining process planning are analyzed from the perspectives of knowledge representation, optimization robustness, data quality, system integration, and industrial deployment. Finally, future development trends are outlined toward more knowledge-enhanced, adaptive, integrated, and practically deployable planning frameworks. This review aims to provide a concise reference for future research and engineering application in intelligent machining process planning.