Open AccessAn intelligence-based optimization of the internal burnishing operation for surface roughness and vicker hardness
Author(s) -
Khanh Can Xuan,
Ba Le Xuan,
An Nguyen Truong,
Hung Trinh Quang,
Thanh Nguyen Trung
Publication year - 2021
Publication title -
tạp chí khoa học giao thông vận tải/transport and communications science journal
Language(s) - English
Resource type - Journals
eISSN - 2615-9554
pISSN - 1859-2724
DOI - 10.47869/tcsj.72.4.1
Subject(s) - burnishing (metal) , surface roughness , machining , vickers hardness test , lubrication , materials science , metallurgy , surface finish , mechanical engineering , composite material , engineering , microstructure , polishing
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.