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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 (Ra), and surface waviness (Wa)—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 Ra (~0.58 µm) and Wa (~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 Ra (>1.0 µm), and substantial waviness (Wa ~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.
Alsoufi et al. (Sat,) studied this question.