Cutting parameters play a major role in improving the energy efficiency of the manufacturing industry. As the main processing method for aviation parts, flank milling usually adopts multi-pass constant and conservative cutting parameters to prevent workpiece deformation but degrades energy efficiency. To address the issue, this paper proposes a novel multi-pass parametric optimisation based on deep reinforcement learning (DRL), allowing parameters to vary to boost energy efficiency under the changing deformation limits in each pass. Firstly, it designs a variable workpiece deformation const.raint on the principle of stiffness decreasing along the passes, based on which it constructs an energy-efficient parametric optimisation model, giving suitable decisions that respond to the varying cutting conditions. Secondly, it transforms the model into a Markov Decision Process and Soft Actor Critic is applied as the DRL agent to cope with the dynamics in multi-pass machining. Among them, an artificial neural network-enabled surrogate model is applied to approximate the real-world machining, facilitating enough explorations of DRL. Experimental results show that, compared with the conventional method, the proposed method improves 45.71% of material removal rate and 32.27% of specific cutting energy while meeting deformation tolerance, which substantiates the benefits of the energy-efficient parametric optimisation, significantly contributing to sustainable manufacturing.

切削参数在提高制造业的能源效率方面发挥着重要作用。作为航空零件的主要加工方法，后刀面铣削通常采用多道次恒定和保守的切削参数来防止工件变形，但降低了能量效率。为了解决这个问题，本文提出了一种基于深度强化学习 (DRL) 的新型多通道参数优化，允许参数在每次通道变化的变形限制下变化以提高能量效率。首先，它根据刚度沿道次递减的原则设计了一个可变的工件变形约束，在此基础上构建了一个节能的参数优化模型，给出了响应变化的切削条件的合适决策。第二，它将模型转换为马尔可夫决策过程，并将 Soft Actor Critic 用作 DRL 代理，以应对多道加工中的动力学。其中，应用人工神经网络代理模型来近似真实世界的加工，促进了对 DRL 的充分探索。实验结果表明，与传统方法相比，所提出的方法在满足变形公差的情况下提高了 45.71% 的材料去除率和 32.27% 的切削比能量，这证实了节能参数优化的好处，显着促进了可持续制造. 应用人工神经网络支持的代理模型来近似真实世界的加工，促进对 DRL 的足够探索。实验结果表明，与传统方法相比，所提出的方法在满足变形公差的情况下提高了 45.71% 的材料去除率和 32.27% 的切削比能量，这证实了节能参数优化的好处，显着促进了可持续制造. 应用人工神经网络支持的代理模型来近似真实世界的加工，促进对 DRL 的足够探索。实验结果表明，与传统方法相比，所提出的方法在满足变形公差的情况下提高了 45.71% 的材料去除率和 32.27% 的切削比能量，这证实了节能参数优化的好处，显着促进了可持续制造.