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<p>Powder metallurgy (PM) superalloys are extensively employed in aerospace applications owing to their exceptional mechanical properties and structural stability. However, the inherently poor thermal conductivity combined with high strength presents substantial challenges during machining, including elevated milling forces and severe tool wear, which substantially impact machining efficiency and surface integrity. To address these issues, nanofluid minimum quantity lubrication (NMQL) was applied to the milling of PM superalloys. A predictive mechanistic model for milling forces, which incorporates lubrication and cooling effects, was developed based on discretized milling theory to elucidate the underlying cutting mechanisms under NMQL conditions. Furthermore, empirical formulas for milling force coefficients under various lubrication regimes were derived through regression analysis of experimental data, and the effects of lubrication conditions on these coefficients were systematically examined. Orthogonal experiments were performed to validate predictive performance of the proposed model. Experimental findings demonstrated that NMQL reduced milling forces by approximately 20% compared to dry cutting, thereby confirming both the accuracy of the model and the superior lubricating and cooling efficacy of NMQL. By integrating theoretical modeling and experimental validation, this study offers a solid foundation for achieving efficient and environmentally sustainable machining of PM superalloys.</p>

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Machining behavior and milling force modeling of powder metallurgy Superalloy under nanofluid minimum quantity lubrication

  • 期刊论文

  • 发表日期：

    2026年2月19日

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  • Xuezhi Wang,
  • Qingyao Liu,
  • Hanying Wang,
  • Xiaoguang Li,
  • Qijia Wang,
  • Guiqiu Song,
  • Minghai Wang

摘要

Powder metallurgy (PM) superalloys are extensively employed in aerospace applications owing to their exceptional mechanical properties and structural stability. However, the inherently poor thermal conductivity combined with high strength presents substantial challenges during machining, including elevated milling forces and severe tool wear, which substantially impact machining efficiency and surface integrity. To address these issues, nanofluid minimum quantity lubrication (NMQL) was applied to the milling of PM superalloys. A predictive mechanistic model for milling forces, which incorporates lubrication and cooling effects, was developed based on discretized milling theory to elucidate the underlying cutting mechanisms under NMQL conditions. Furthermore, empirical formulas for milling force coefficients under various lubrication regimes were derived through regression analysis of experimental data, and the effects of lubrication conditions on these coefficients were systematically examined. Orthogonal experiments were performed to validate predictive performance of the proposed model. Experimental findings demonstrated that NMQL reduced milling forces by approximately 20% compared to dry cutting, thereby confirming both the accuracy of the model and the superior lubricating and cooling efficacy of NMQL. By integrating theoretical modeling and experimental validation, this study offers a solid foundation for achieving efficient and environmentally sustainable machining of PM superalloys.