An intelligence-based optimization of the internal burnishing operation for surface roughness and vicker hardness

Abstract

Boosting machining quality is a prominent solution to save production costs for burnishing operations. In this work, a machining condition-based optimization has been performed to decrease surface roughness (SR) and enhance Vickers hardness (VH) of the minimum quantity lubrication-assisted burnishing operation (MQLABO). The burnishing factors are the spindle speed (S), depth of penetration (D), and the air pressure (P). The burnishing trails of the hardened material labeled 40X have been conducted on a milling machine. The adaptive neuro-based-fuzzy inference system (ANFIS) was used to construct the correlations between the process inputs and MQLABO responses. The non-dominated sorting genetic algorithm-II (NSGA-II) is utilized to determine the optimal parameters. The scientific outcomes revealed that the optimal values of the S, D, and P are 800 RPM, 0.09 mm, and 4.0 Bar, respectively. The SR is decreased by 53.8%, while the VH is enhanced by 3.1%, respectively, as coBarred to the initial values.