Sustainable machining has become increasingly important due to the growing demand for energy-efficient and environmentally responsible manufacturing. AISI S1 tool steel, widely used in hot-work tooling applications, requires hard turning operations where lubrication strategy and hardness significantly influence machining performance and sustainability outcomes. This study investigates the combined effects of workpiece hardness and Minimum Quantity Lubrication (MQL) on power consumption, vibration, and surface roughness in the hard turning of AISI S1 steel. A Taguchi L36 orthogonal design was applied to efficiently examine the influence of feed rate, cutting speed, depth of cut, hardness levels (51 and 54 HRC), and cooling strategies (dry, flood, and MQL). The results of the analysis of variance revealed that feed rate was the dominant factor affecting surface roughness (≈70%), whereas depth of cut largely governed energy consumption (≈34%). MQL significantly reduced power demand (up to 18%) and vibration levels (≈20%) while improving surface quality (≈60%) compared to dry machining. Predictive regression models established strong correlations between vibration, power signals, and surface integrity ( R 2 > 0.95). A weighted multi-response sustainability optimization identified the most eco-efficient condition at 54 HRC under MQL. The key limitations of this study include analyzing only two hardness levels and the absence of tool-wear evaluation. Overall, this research contributes to a comprehensive sustainability-oriented machining framework by integrating hardness control, efficient lubrication, and multi-criteria decision analysis for the hard turning of AISI S1 steel.
Yavuz et al. (Tue,) studied this question.