Abstract
This study comprehensively investigates the determination of chip reduction coefficient (CRC) and von Mises stress (VMS) during dry turning of Nickel-Chromium case-hardened steel (EN36C), renowned for its high surface hardness and core toughness. Machining parameters, including cutting speed (36-100 m/min), feed rate (0.49-0.86 mm/rev), and depth of cut (0.67-1.5 mm), were rigorously analyzed using Analysis of Variance (ANOVA) and Artificial Neural Networks. ANOVA identified cutting speed as the most influential factor, accounting for 52.04% of CRC and 35.04% of VMS variations, with feed rate and depth of cut also playing significant roles. ANN modeling achieved a correlation coefficient of 0.97, demonstrating excellent predictive accuracy for parameter optimization. Scanning Electron Microscopy revealed chip morphology, showing continuous chips under optimal conditions of high cutting speed (100 m/min), low feed rate (0.63 mm/rev), and moderate depth of cut (1.0 mm), minimizing stress and enhancing material removal efficiency. Brittle chips were observed at lower speeds (36 m/min) and higher feed rates, emphasizing the critical role of parameter selection. Optimal machining parameters significantly improved surface quality, reduced tool wear, and minimized operational stresses. This research offers a robust framework for machining process optimization, with implications for enhancing industrial efficiency and cost-effectiveness.