ABSTRACT

Sustainable machining processes are efficiently achieved using the selection of optimal parameters. In this study, the minimum quantity lubrication-assisted multi-roller burnishing (MQLAMRB) operation is proposed and optimized to reduce the total energy consumption (TE), mean roughness depth (MR), and roundness deviation (RN). Burnishing parameters are the burnishing speed (BS), depth of penetration (DOP), the quantity consumed of the lubricant (QO), and the pressure value of the compressed air (PA). The embodied energy of the lubricant (E_el) and burnishing tool (E_eb) are developed and integrated into the TE model. The artificial neural network (ANN) model of the energy consumption in the burnishing time (E_bo), MR, and RN is proposed regarding the MQLAMRB parameters. The best-selected solution is determined using an efficient glowworm swarm optimization (GSO) algorithm and the TOPSIS. The results indicated that the 4–25-21-25-3 ANN structure effectively used to construct the MQLAMRB performances. The optimal outcomes of the BS, DOP, QO, and PA are 94 m/min, 0.12 mm, 130 ml/h, and 0.7 MPa, respectively. Moreover, the TE, MR, and RN are decreased by 12.2%, 14.2%, and 42.5%, respectively. The reductions in the MR and RN of the burnished surface are 90.23% and 88.18%, respectively, as compared to the pre-machined conditions.

摘要

通过选择最佳参数有效地实现可持续加工过程。在这项研究中，提出并优化了最小量润滑辅助多辊抛光 (MQLAMRB) 操作，以降低总能耗 (TE)、平均粗糙深度 (MR) 和圆度偏差 (RN)。抛光参数是抛光速度 (BS)、穿透深度 (DOP)、润滑剂消耗量 (QO) 和压缩空气压力值 (PA)。润滑剂 (E _el ) 和抛光工具 (E _eb ) 的蕴含能量被开发并集成到 TE 模型中。抛光时间能耗（E _{bo ）的人工神经网络（ANN）模型})、MR 和 RN 是针对 MQLAMRB 参数提出的。使用有效的萤火虫群​​优化 (GSO) 算法和 TOPSIS 确定最佳选择的解决方案。结果表明，4-25-21-25-3 ANN 结构有效地用于构建 MQLAMRB 性能。BS、DOP、QO 和 PA 的最佳结果分别为 94 m/min、0.12 mm、130 ml/h 和 0.7 MPa。此外，TE、MR 和 RN 分别下降了 12.2%、14.2% 和 42.5%。与预加工条件相比，抛光表面的 MR 和 RN 分别减少了 90.23% 和 88.18%。