Abstract Surface integrity is crucial in CNC machining, as it significantly impacts dimensional accuracy, fatigue life, and overall functional performance. This study examines the impact of thermal conductivity and hardness on surface roughness and waviness in five materials: Aluminum Alloy 6061, Brass C26000, Bronze C51000, Carbon Steel 1020 (annealed), and Stainless Steel 304 (annealed). All were machined under identical dry turning conditions to isolate material effects. The novelty lies in the combined analysis of roughness and waviness metrics, ISO-based profile ratios ( R q / R a , R t / R z , W q / W a , W t / W z ) that benchmark surface uniformity, and statistical shape descriptors (skewness, kurtosis) that indicate whether lubricant-retaining valleys or stress-concentrating asperities dominate surfaces, thereby extending beyond existing multi-material studies that primarily rely on mean roughness comparisons under identical cutting parameters. These metrics provide functional insights: negative skewness enhances tribological performance, while high kurtosis highlights asperities linked to fatigue crack initiation. Results show Stainless Steel 304 achieved the smoothest finish ( R a ≈ 0.9 μm, W a ≈ 0.6 μm), while Carbon Steel 1020 produced the roughest ( R a ≈ 4.1 μm). A ± 10% change in thermal conductivity consistently shifted R a by 5.8–6.3%. This study introduces a function-oriented, metrology-informed framework that translates surface descriptors into predictive indicators of wear resistance, fatigue performance, and dimensional reliability in high-precision manufacturing.
Alsoufi et al. (Mon,) studied this question.