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<p>In the machining of difficult-to-cut materials, the adoption of minimum quantity lubrication (MQL) technology has become a viable alternative to traditional machining. And the matching of new MQL enhancement technology with cutting parameters plays an important role in improving the machining performance. This work conducted an experiment on optimization of cutting parameters under MQL using GnP-ZrO<sub>2</sub> hybrid nanofluids (NFs) in turning of GH4169 and the response surface method (RSM) was employed to obtain the regression model for surface roughness (R<sub>a</sub>), cutting temperature (T), and tool wear (<i>VB</i>). The results of the analysis of variance (ANOVA) showed that the accuracy of model was relatively high. The optimal of values for the four input parameters were obtained (<i>v</i><sub><i>c</i></sub>: 75&#xa0;m/min, <i>a</i><sub><i>p</i></sub>: 0.05&#xa0;mm, <i>f</i><sub><i>z</i></sub>: 0.15&#xa0;mm/r, <i>NFs</i>: 5 wt.%). The use of green hybrid NFs and the optimization of cutting parameters can improve the machining surface quality by 45.85% and reduce the cutting temperature and tool wear by 33.45% and 24.29%, respectively. Moreover, hybrid NFs can help to form a continuous and uniform protective friction film, reducing adhesive wear.</p>

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Performance Evaluation of GnP-ZrO2 Hybrid Nanofluids in Turning GH4169 Based on RSM

  • 期刊论文

  • 发表日期：

    2025年9月30日

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  • Jinhao Ma,
  • Enzhao Cui,
  • Guangming Zheng,
  • Xiang Cheng,
  • Lei Gu,
  • Wei Li

摘要

In the machining of difficult-to-cut materials, the adoption of minimum quantity lubrication (MQL) technology has become a viable alternative to traditional machining. And the matching of new MQL enhancement technology with cutting parameters plays an important role in improving the machining performance. This work conducted an experiment on optimization of cutting parameters under MQL using GnP-ZrO2 hybrid nanofluids (NFs) in turning of GH4169 and the response surface method (RSM) was employed to obtain the regression model for surface roughness (Ra), cutting temperature (T), and tool wear (VB). The results of the analysis of variance (ANOVA) showed that the accuracy of model was relatively high. The optimal of values for the four input parameters were obtained (vc: 75 m/min, ap: 0.05 mm, fz: 0.15 mm/r, NFs: 5 wt.%). The use of green hybrid NFs and the optimization of cutting parameters can improve the machining surface quality by 45.85% and reduce the cutting temperature and tool wear by 33.45% and 24.29%, respectively. Moreover, hybrid NFs can help to form a continuous and uniform protective friction film, reducing adhesive wear.