The execution of sustainable manufacturing methods to make machining processes more eco-friendly is a difficult task that has attracted significant attention from the industrial area for a long time. As one of the leading manufacturing processes, machining can have a profound impact on the environment, society, and financial aspects. In a specific scenario, recognizing reasonable machining conditions to supply cutting fluids utilizing eco-friendly methods is at present a significant focal point of academic and industrial sector research. This study is to investigate the optimal operational parameters such as speed, feed rate, and cutting depth during high-speed machining of 15CDV6 HSLA steel under near-dry (green machining) and flood lubrication using response surface methodology and an artificial neural network that leads to better performance measures like tool-chip interface temperature, specific energy, yield strength, and percentage elongation. Initially, tensile samples were prepared on wire EDM, further high-speed machining has been carried out on CNC milling using a mechanical carbide cutter to improve performance. The results showed that an improvement in tool-chip interface temperature (0.9–12%), specific energy (0.8–12%), yield strength (1.8–3.2%), and percentage elongation (1.0–8.9%) using green machining has been witnessed and confirmed that green machining is an alternative of the flood to enhance the strength while reducing the specific energy in addition to eco-friendly. Moreover, the comparative analysis between RSM and ANN results determined that the ANN delivers more precise results and confirms its adequacy when its correlation coefficients are large, and root mean square errors are small compared to those obtained through the RSM.

执行可持续的制造方法以使加工过程更加环保，这是一项艰巨的任务，长期以来一直引起工业领域的广泛关注。作为领先的制造工艺之一，机加工会对环境，社会和财务方面产生深远的影响。在特定情况下，使用环保方法识别合理的加工条件以供应切削液是当前学术和工业领域研究的重要重点。这项研究旨在研究最佳操作参数，例如速度，进给速度，响应面方法和人工神经网络在近干（绿色加工）和泛滥润滑条件下对15CDV6 HSLA钢进行高速加工时的切削深度和切削深度，从而获得了更好的性能指标，例如刀具-芯片界面温度，比能量，屈服强度和伸长率百分比。最初，在电火花线切割机上制备了拉伸样品，然后使用机械硬质合金刀具在CNC铣削上进行了进一步的高速加工以提高性能。结果表明，工具-芯片界面温度（0.9–12％），比能量（0.8–12％），屈服强度（1.8–3.2％）和伸长率（1.0–8）均有改善。9％的受访者已经见证了使用绿色机械加工的事实，并证实绿色机械加工是替代洪水的一种替代方法，不仅可以提高强度，而且还可以降低比能源，同时还具有生态友好性。此外，通过对RSM和ANN结果的比较分析，可以确定，与通过RSM获得的相关系数相比，当ANN的相关系数较大且均方根误差较小时，ANN可以提供更准确的结果并确认其适当性。