Energy modelling and multi-objective tool path optimisation: an energy-efficient manufacturing approach for drill-reaming hybrid machining
针对钻铰复合加工中多直径孔加工的能量消耗问题，提出了基于灰狼优化算法的能量预测与刀具路径优化模型，在案例中总能耗降低7.80%，加工时间减少4.88%。
This study presents an energy modelling and tool path optimisation method for drill-reaming hybrid machining to advance energy-efficient manufacturing. Drilling, one of the most widely used machining processes, spends approximately 70 % of its cycle time on tool movement and switching, making energy optimisation essential. However, energy modelling and energy-saving strategies for multi–diameter hole machining via drill–reaming hybrid processes have received little attention. To address this gap, this paper develops precise energy prediction and tool path optimisation models, which are solved using the Gray Wolf Optimisation (GWO) algorithm, enabling the identification of tool paths that minimise both energy consumption and machining time. Furthermore, case study results show that the GWO-optimised strategy reduces total energy consumption by 39834 J (7.80 %) and total machining time by 23.2 s (4.88 %) compared to conventional empirical methods. Moreover, the optimisation completes in just 5.73s of computation on a computer equipped with an Intel Core i5–13600KF CPU and an NVIDIA RTX 3070 GPU, underscoring its efficiency and industrial applicability.