Abstract
This study aims to analyze the influence of cutting parameters (cutting speed, feed rate, and depth of cut) on cutting temperature, forces, and energy consumption during the dry turning of Ti6Al4V, providing an optimized machining strategy to improve efficiency and sustainability. Due to the challenges of machining this alloy, such as high temperatures and tool wear, response surface methodology (RSM) was used to develop second-degree polynomial models, and analysis of variance (ANOVA) identified the most influential factors. The results indicate that depth of cut has the highest impact on cutting temperature (42.59%), cutting forces (53.08%, 74.73%, and 48.87% in the respective force components), and power consumption (49.78%), while feed rate is the dominant factor in energy consumption (63.36%). Gray relational analysis (GRA) was applied to optimize machining conditions based on the developed models, allowing a wider selection of cutting parameters beyond the experimental values. These findings provide a valuable tool for the industry, offering manufacturers a data-driven approach to optimizing the machining of Ti6Al4V and reducing energy consumption and tool wear while improving process stability. The proposed methodology enhances sustainability and cost-efficiency in titanium alloy machining, particularly in the aeronautical sector.