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<p>Inconel 718, a nickel-based superalloy essential for aerospace components, presents significant machining challenges due to excessive cutting forces and residual stresses that impair surface quality and dimensional precision. This study introduces a novel multi-objective optimization framework for milling Inconel 718, pioneering the first use of C60 nanofluid minimum quantity lubrication (NMQL) to leverage the exceptional thermal conductivity and lubricity of C60 nanoparticles. A coupled prediction model for cutting force and residual stress, incorporating NMQL’s lubrication and cooling effects, was developed and optimized using a diversified mutation-driven particle swarm optimization (MOPSO) algorithm to balance cutting force, residual stress, and material removal rate (MRR). Experimental validation on a vertical machining center confirms high model accuracy, with cutting force prediction errors below 6.73% and residual stress errors under 20 MPa. Compared to conventional MQL, C60 NMQL reduces cutting force by 24.92%, residual stress by 36.73%. These results underscore C60 NMQL’s ability to mitigate mechanical and thermal loads, providing a robust quantitative framework for achieving high-quality, efficient machining of nickel-based superalloys in aerospace applications.</p>

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Optimizing milling parameters for Inconel 718 with C60 nanofluid lubrication

  • 期刊论文

  • 发表日期：

    2026年3月30日

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  • Zhirong Pan,
  • Bin Yao,
  • Zhiqin Cai,
  • Hao Sun,
  • Qixin Lan,
  • Jinhui Zhang

摘要

Inconel 718, a nickel-based superalloy essential for aerospace components, presents significant machining challenges due to excessive cutting forces and residual stresses that impair surface quality and dimensional precision. This study introduces a novel multi-objective optimization framework for milling Inconel 718, pioneering the first use of C60 nanofluid minimum quantity lubrication (NMQL) to leverage the exceptional thermal conductivity and lubricity of C60 nanoparticles. A coupled prediction model for cutting force and residual stress, incorporating NMQL’s lubrication and cooling effects, was developed and optimized using a diversified mutation-driven particle swarm optimization (MOPSO) algorithm to balance cutting force, residual stress, and material removal rate (MRR). Experimental validation on a vertical machining center confirms high model accuracy, with cutting force prediction errors below 6.73% and residual stress errors under 20 MPa. Compared to conventional MQL, C60 NMQL reduces cutting force by 24.92%, residual stress by 36.73%. These results underscore C60 NMQL’s ability to mitigate mechanical and thermal loads, providing a robust quantitative framework for achieving high-quality, efficient machining of nickel-based superalloys in aerospace applications.