首页|Integrated optimisation of multi-pass cutting parameters and tool path with hierarchical reinforcement learning towards green manufacturing

Integrated optimisation of multi-pass cutting parameters and tool path with hierarchical reinforcement learning towards green manufacturing

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Five-axis machining, especially flank milling, is popular in machining thin-walled freeform surface parts with high energy consumption. Reducing the machining energy consumption is paramount for advancing green manufacturing. Therefore, this paper proposes an energy-efficient integration optimisation of cutting parameters and tool path with hierarchical reinforcement learning (HRL). Firstly, a novel multi-pass machining energy consumption model is developed with cutting and path parameters, based on which the integrated optimisation problem is modelled considering a dynamic workpiece deformation constraint. Secondly, HRL with a Soft Actor Critic agent (HSAC) decouples the model into two Markov Decision Processes at different timescales. The higher-layer plans cutting parameters for each pass on a macro timescale, while the micro-timescale lower-layer performs multiple tool path expansions with the planned cutting parameters, and provides feedback to the higher layer. By hierarchical optimisation and non-hierarchical interaction, the model is efficiently solved. Moreover, curriculum transfer learning is applied to expedite task completion of the lower layer, enhancing interaction efficiency between the two layers. Experiments show that, compared with two benchmarks, the proposed method improves machining energy consumption by 35.02 % and 30.92 %, and reduces machining time by 38.57 % and 27.17%, providing a promising paradigm of green practices for thin-walled freeform parts and the broader manufacturing industry.

Integration optimisationProcess planningHierarchical reinforcement learningEnergy efficiencyComplex partsGreen manufacturing

Fengyi Lu、Guanghui Zhou、Chao Zhang、Yang Liu、Marco Taisch

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School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China||State Key Laboratory for Manufacturing Systems Engineering, Xi 'an Jiaotong University, Xi 'an, 710054, China

Department of Management and Engineering, Linkoeping University, SE-581 83 Linkoeping, Sweden||Industrial Engineering and Management, University of Oidu, 90570 Oulu, Finland

Department of Management, Economics and Industrial Engineering, Politecnico di Milano, 20133 Milan, Italy

2025

Robotics and computer-integrated manufacturing

Robotics and computer-integrated manufacturing

SCI
ISSN:0736-5845
年,卷(期):2025.91(Feb.)
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