Optimizing grinding parameters for surface integrity in single crystal nickel superalloys using SVM modeling

This research explores the intricate dynamics of machining nickel-based single crystal superalloys, with a focused examination of the principal parameters influencing grinding forces and surface roughness. It marries micro-scale grinding simulations with sophisticated Support Vector Machine (SVM) modeling in Matlab, conducting an in-depth analysis of how variables such as grinding depth, abrasive grain size, spindle speed, and feed rate affect the surface integrity of these premium materials. The introduction of the SVM model represents a novel contribution to the field, undergoing extensive validation against traditional predictive methods and demonstrating superior predictive accuracy. Detailed investigation into grinding parameters uncovers a significant link between process variables and the microstructural alterations on the alloy’s surface, leading to the determination of grinding conditions that substantially enhance surface quality. The accuracy of the SVM model in predicting surface roughness, evidenced by a Mean Squared Error (MSE) of 9.9662e-05 and an R-squared value of 99.9992%, positions it as a pivotal tool for process optimization. This, in turn, offers substantial advantages for precision engineering in aerospace component manufacturing. The findings underscore the transformative impact of SVM-based modeling in realizing exceptional performance in the machining of complex materials.