Hamıd Maher Waleed Hamıd, Özlü Barış, Ulaş Hasan Basri, Demir Halil
Department of Manufacturing Engineering, Institute of Graduate Education, Karabük University, Karabük, 78050, Turkey.
Department of Mechanical Program, Aksaray University, Aksaray, 68100, Turkey.
Sci Rep. 2025 Sep 26;15(1):33309. doi: 10.1038/s41598-025-04198-8.
The present study focuses on the effects of cooling/lubrication conditions and cutting parameters on energy consumption (EC), carbon emissions (CE), surface roughness (Ra), cutting temperature (T), tool wear (Vb) and vibration (Vib) in sustainable milling of Inconel 718 alloy. Also, it was aimed to estimate the EC, CE, Ra, T and Vib values obtained in milling experiments using three different regression-based machine learning (ML) models. The performances of the models used in ML were compared using R-squared, MSE and MAPE performance criteria. In the experiments conducted by reducing the feed rate and cutting speed and in MQL machining conditions, it was observed that EC and CE values reached minimum values. In MQL machining conditions, it was observed that the lowest Ra values were achieved at high cutting speed and low feed rate. The lowest Vb was measured at low cutting speed and feed rate in air machining conditions. Increasing the cutting speed and decreasing the feed rate in MQL machining conditions had a positive effect on Vib. In the MQL machining condition, at 40 m/min cutting speed and 0.06 mm/rev feed rate, the lowest energy consumption and carbon emission were 0.76 kJ/s and 0.54796 kg-CO respectively. The lowest surface roughness and vibration values were measured as 0.234 μm and 1.91 mm/s respectively, at 80 m/min cutting speed and 0.06 mm/rev feed rate in MQL machining condition. The lowest cutting temperature was measured as 31 °C at a cutting speed of 40 m/min and a feed rate of 0.06 mm/rev under air machining conditions. It was seen that the EC, CE, Ra, T and Vib values arising from the input parameters in the machining of Inconel 718 alloy could be successfully predicted using three different regression-based ML models.
本研究聚焦于冷却/润滑条件和切削参数对因科镍合金718可持续铣削过程中的能量消耗（EC）、碳排放（CE）、表面粗糙度（Ra）、切削温度（T）、刀具磨损（Vb）和振动（Vib）的影响。此外，旨在使用三种不同的基于回归的机器学习（ML）模型估计铣削实验中获得的EC、CE、Ra、T和Vib值。使用决定系数、均方误差和平均绝对百分比误差性能标准比较了ML中使用的模型的性能。在通过降低进给速度和切削速度进行的实验以及微量润滑（MQL）加工条件下，观察到EC和CE值达到最小值。在MQL加工条件下，观察到在高切削速度和低进给速度下可获得最低的Ra值。在干式加工条件下，在低切削速度和进给速度下测量到最低的Vb。在MQL加工条件下提高切削速度并降低进给速度对Vib有积极影响。在MQL加工条件下，切削速度为40 m/min、进给速度为0.06 mm/rev时，最低能量消耗和碳排放分别为0.76 kJ/s和0.54796 kg-CO。在MQL加工条件下，切削速度为80 m/min、进给速度为0.06 mm/rev时，最低表面粗糙度和振动值分别测量为0.234μm和1.91 mm/s。在干式加工条件下，切削速度为40 m/min、进给速度为0.06 mm/rev时，最低切削温度测量为31°C。可以看出，使用三种不同的基于回归的ML模型可以成功预测因科镍合金718加工中输入参数产生的EC、CE、Ra、T和Vib值。
