Five-axis Multi-station Process Planning Based on Multi-task Graph Reinforcement Learning

Authors

• Junjie Liu, Faculty of Mechanical Engineering & Mechanics, Ningbo University, Ningbo 315211, China
• Jianlei Xu, Hanggong Intelligent Technology Group Co., Ltd., Ningbo 315048, China
• Litao Ju, Hanggong Intelligent Technology Group Co., Ltd., Ningbo 315048, China
• Yanhai Hu, Faculty of Mechanical Engineering & Mechanics, Ningbo University, Ningbo 315211, China
• Qinghao Zhang, Hanggong Intelligent Technology Group Co., Ltd., Ningbo 315048, China
• Qiang Liu, Hanggong Intelligent Technology Group Co., Ltd., Ningbo 315048, China

Abstract

Abstract: A hierarchical intelligent planning architecture integrating multi-task graph and masked proximal policy optimization (MTG-MPPO) is proposed to address the challenges of factorial expansion of the state space and multi-objective interference in five-axis multi-feature machining. An attributed adjacency graph integrating boundary representation and machining direction vectors is constructed. Probabilistic soft labels are output through a multi-task network to quantify process flexibility and thus achieve prior topological pruning of solution space. An MDP model integrating directed acyclic graph masks and dense reward shaping is built. A Monte Carlo (MC) optimization mechanism based on policy distribution is introduced to coordinate cutting, tool-changing, and rotary-table costs. Simulation results show that MTG-MPPO effectively suppresses disorderly spatial shuttling, reducing the total machining time by 12.33% and 9.81% compared with heuristic baseline algorithms. Under millisecond-level inference, the model escapes local barriers with a maximum probability of 39% to approach better solutions. This validates the computational substitution paradigm that replaces physical machine tool running time with virtual inference, providing a highly robust path for process decision-making in digital twin environments.

Recommended Citation

Liu, Junjie; Xu, Jianlei; Ju, Litao; Hu, Yanhai; Zhang, Qinghao; and Liu, Qiang (2026) "Five-axis Multi-station Process Planning Based on Multi-task Graph Reinforcement Learning," Journal of System Simulation: Vol. 38: Iss. 10, Article 10.
DOI: 10.16182/j.issn1004731x.joss.26-0218
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss10/10

First Page

2924

Last Page

2938

CLC

TP391.9

Recommended Citation

Liu Junjie, Xu Jianlei, Ju Litao, et al. Five-axis Multi-station Process Planning Based on Multi-task Graph Reinforcement Learning[J]. Journal of System Simulation, 2026, 38(10): 2924-2938.

Corresponding Author

Hu Yanhai

DOI

10.16182/j.issn1004731x.joss.26-0218