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This study investigates the optimization of material removal rate (MRR) in the hard milling process of 90CrSi tool steel using a minimum quantity lubrication (MQL) technique enhanced with copper oxide (CuO) nanoparticles. A five-factor, three-level Box–Behnken Design (BBD) was employed to examine the effects of tool diameter (Dt), cutting speed (vc), feed per tooth (fz), depth of cut (ap), and nanoparticle size (Davg) on MRR. The experiments were conducted on hardened 90CrSi steel with the integration of nano-lubricants to improve lubrication and cooling performance. A second-order regression model was developed and validated with high accuracy (R2 = 0.9671), demonstrating its suitability for predicting MRR. The analysis of variance (ANOVA) and main effect plots revealed that feed per tooth and depth of cut were the most influential parameters, while tool diameter had a significant negative effect. The optimal combination of input parameters yielded a maximum MRR of 2663.22 mm3/min. These findings provide practical insights into improving productivity and sustainability in hard milling operations using environmentally friendly lubrication strategies.

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Enhanced MRR in Hard Milling Using CuO Nano-Lubricants: Experimental and RSM-Based Modeling Study

  • 会议论文

  • 发表日期：

    2026年

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  • Hoang Van Got,
  • Do The Vinh,
  • Vu Ngoc Pi,
  • Dinh Van Thanh,
  • Hoang Anh Toan

摘要

This study investigates the optimization of material removal rate (MRR) in the hard milling process of 90CrSi tool steel using a minimum quantity lubrication (MQL) technique enhanced with copper oxide (CuO) nanoparticles. A five-factor, three-level Box–Behnken Design (BBD) was employed to examine the effects of tool diameter (Dt), cutting speed (vc), feed per tooth (fz), depth of cut (ap), and nanoparticle size (Davg) on MRR. The experiments were conducted on hardened 90CrSi steel with the integration of nano-lubricants to improve lubrication and cooling performance. A second-order regression model was developed and validated with high accuracy (R2 = 0.9671), demonstrating its suitability for predicting MRR. The analysis of variance (ANOVA) and main effect plots revealed that feed per tooth and depth of cut were the most influential parameters, while tool diameter had a significant negative effect. The optimal combination of input parameters yielded a maximum MRR of 2663.22 mm3/min. These findings provide practical insights into improving productivity and sustainability in hard milling operations using environmentally friendly lubrication strategies.