Enhancing dry hard turning of Inconel 925: RSM optimization and performance analysis

The Inconel superalloy family, which is based on nickel and chromium, is renowned for its exceptional mechanical capabilities at high temperatures and strong resistance to oxidation and corrosion. Among the most well-known grades are Inconel 625, 718, 600, and so on. Inconel 925 is a good choice for applications demanding corrosion resistance in addition to high-strength requirements. There is difficulty in machining Inconel 925 due to low heat conductivity, high hardness, significant chemical reaction with tools at elevated temperatures, and low elastic modulus. Optimizing the various machining conditions to achieve the best machinability criteria for machining Inconel 925 is essential. This study explores the application of response surface methodology (RSM) in analyzing the performance of machining operations with five levels of feed, spindle rate, and depth of cut. The finite-element method (FEM) was utilized to conduct numerical modeling based on the RSM experimental design, with experiments carried out in a dry working environment on a heavy-duty lathe. The study focused on predicting cutting forces, surface roughness, and tool wear. The experimental results showed that the predicted cutting forces closely matched the observed values, with minimal error between the numerical modeling and experimental data. The error between predicted and experimental surface roughness ranged from 0.07% to 5.52%, while the tool wear error varied between 0.18% and 5.95%. The significance of the second-order quadratic models was validated by p-values less than .05, confirming their relevance. The model's goodness of fit was also confirmed by high determination coefficients (R²), indicating a strong correlation between the predicted and experimental data. The quadratic and numerical models proved effective in estimating performance metrics across various machining scenarios, although their applicability is limited to specific machining conditions. This work demonstrates the potential of RSM and FEM in optimizing machining processes and provides valuable insights for future studies in manufacturing.