Predictive Modeling of Surface Integrity and Material Removal Rate in Computer Numerical Control Machining: Effects...

This study investigates the influence of thermal conductivity and hardness on computer numerical control (CNC) turning performance, focusing on key machining metrics-material removal rate (MRR), surface roughness (R_a), and surface waviness (W_a)-across five engineering materials: aluminum 6061, brass C26000, bronze C51000, carbon steel 1020, and stainless steel 304. Experimental results reveal a strong correlation between material properties and machining efficiency. Materials with high thermal conductivity (>100 W/m·K) exhibited up to 38% higher MRR and improved surface integrity compared to low-conductivity counterparts. Aluminum 6061 achieved the highest MRR (7.5 mm³/min at a 0.25 mm/rev feed rate), with the lowest R_a (~0.58 µm) and W_a (~0.4576 µm), confirming its excellent machinability and heat dissipation. Conversely, stainless steel 304, characterized by low thermal conductivity (16 W/m·K) and high hardness (210 HBW), recorded the lowest MRR (1.125 mm³/min), elevated R_a (>1.0 µm), and substantial waviness (W_a ~0.9442 µm), indicating severe tool wear and thermal deformation. A multivariable regression model incorporating cutting speed, feed rate, thermal conductivity, and hardness was developed to predict MRR, achieving high predictive accuracy (R² > 0.92) for high-conductivity materials. Deviations of ±0.5 mm³/min were observed in harder, low-conductivity materials due to nonlinear effects such as strain hardening and thermal expansion. Measurement uncertainty analysis, with an estimated expanded uncertainty of ±2.5% for MRR and ±0.02 µm for surface metrics, ensures the reliability of these findings. These results underscore the importance of material-specific machining parameter optimization to enhance productivity, surface quality, and tool longevity in high-precision industries, including aerospace, automotive, and biomedical manufacturing.