Prediction model for specific cutting energy of nickel-based Inconel 718 under NMQL condition

Abstract The cutting characteristics of Inconel 718 alloy are high hardness and surface hardening, resulting in fast tool wear, severe chipping, and inadequate machining accuracy. To overcome these challenges, this article proposes a method to enhance the cutting performance by injecting fullerene C60 nanoparticle cutting fluid with minimum quantity lubrication (MQL) into the cutting zone. Leveraging the Johnson-Cook constitutive model and the imaginary heat source method, this study simulates the cooling effect and friction reduction characteristics of the cutting contact interface under minimum quantity lubrication conditions, and assessment of cutting energy consumption using predicted and measured specific cutting energy (SCE). Through friction wear tests, the friction coefficient changes under various lubrication conditions are measured and analyze the impact of lubrication conditions on friction and wear mechanism. The cutting test results reveal that variations in cutting parameters significantly influence energy efficiency, with specific cutting energy exhibiting a downward trend as the material removal rate (MRR) increases. Notably, C60 nanoparticle minimum quantity lubrication (NMQL) stands out excellent friction reduction and cooling effects among other lubrication methods. Experimental data demonstrate that NMQL compared with dry cutting, flood cutting and pure MQL, the specific cutting energy is reduced by 31.3%, 19.13%, and 17.37%, respectively, and the cutting energy performance is significantly improved. The maximum error of the SCE prediction model is 17.5%, and the prediction results align well with the experimental findings. This article offers fresh insights for advancing machining theory and exploring sustainable green machining of nickel-based alloys.