Analysis of the impact of cutting conditions on the average thermal load during hard machining of C65 Steel ± 2 HRC (DIN) with blade radius from R = 1.6 to 2.4

This study investigates the thermal dynamics of the turning process for C65 steel, focusing on the influence of various machining parameters on temperature rise. Integrating experimental methods and statistical analysis, the research examines the effects of cutting speed (v), feed rate (f), depth of cut (a), and cutting insert tip radius (r=2.4mm) on the average temperature in the cutting zone. Experiments were conducted on heat-treated C65 steel rings, using a custom fixture and a modified Kennametal insert holder to ensure precise temperature measurements, collected via a natural thermocouple method. The average temperature was measured by a natural thermocouple and statistically processed by computer software CADEX developed at the Faculty of Mechanical Engineering in Skopje. The findings reveal that cutting speed is the most significant factor influencing temperature rise, followed by feed rate, while depth of cut has a minor impact. Notably, the radius of the cutting insert tip is inversely proportional to the temperature, with an increase in the tip radius resulting in a decrease in average temperature. This inverse relationship underscores the potential for optimizing tool design to enhance heat dissipation and reduce thermal stress on the cutting tool. A fourth-degree polynomial model was developed through regression analysis to correlate thermovoltage (V) with temperature (T), providing a quantitative framework for predicting thermal behavior. The model highlights that cutting speed has the greatest effect on temperature rise, with the highest recorded temperature being 915.84°C. Conversely, a larger radius of the cutting insert tip corresponds to lower average temperatures, stabilizing around 829.53°C. These insights offer actionable strategies for optimizing machining processes. By strategically adjusting cutting parameters, particularly cutting speed and feed rate, manufacturers can achieve significant improvements in tool life and product quality. The developed mathematical model serves as a predictive tool for fine-tuning machining conditions, ensuring a balance between productivity and tool longevity. This research contributes substantially to the understanding of thermal dynamics in machining processes. It provides a comprehensive analysis and robust modeling of the effects of cutting parameters on temperature rise, supported by rigorous experimental data and statistical validation. The study underscores the importance of considering thermal effects in machining to optimize efficiency, tool life, and product quality, offering valuable insights for both academic research and practical applications in the field of machining and process optimization.