A Closer Look at Precision Hard Turning of AISI4340: Multi-Objective Optimization for Simultaneous Low Surface Roughness and High Productivity

Abstract

This article reports an extended investigation into the precision hard turning of AISI 4340 alloy steel when machined by two different types of inserts: wiper nose and conventional round nose. It provides a closer look at previously published work and aims at determining the optimal process parameters for simultaneously minimizing surface roughness and maximizing productivity. In the mathematical models developed by the authors, surface roughness at different cutting speeds, depths of cut and feed rates is treated as the objective function. Three robust multi-objective techniques, (1) multi-objective genetic algorithm (MOGA), (2) multi-objective Pareto search algorithm (MOPSA) and (3) multi-objective emperor penguin colony algorithm (MOEPCA), were used to determine the optimal turning parameters when either the wiper or the conventional insert is used, and the results were experimentally validated. To investigate the practicality of the optimization algorithms, two turning scenarios were used. These were the machining of the combustion chamber of a gun barrel, first with an average roughness (Ra) of 0.4 µm and then with 0.8 µm, under conditions of high productivity. In terms of the simultaneous achievement of both high surface quality and productivity in precision hard turning of AISI 4340 alloy steel, this work illustrates that MOPSA provides the best optimal solution for the wiper insert case, and MOEPCA results are the best for the conventional insert. Furthermore, the results extracted from Pareto front plots show that the wiper insert is capable of successfully meeting both the requirements of Ra values of 0.4 µm and 0.8 µm and high productivity. However, the conventional insert could not meet the 0.4 µm Ra requirement; the recorded global minimum was Ra = 0.454 µm, which reveals the superiority of the wiper compared to the conventional insert.

Machinability characteristics analysis of hard turning operation on AISI 4340 steel using physical vapor deposition multilayer coated carbide cutting tool in the dry environment

“…68 Infrared thermography of cutting temperature during run nos. 5,6,8,12,18,20,23,25, and 27 are shown in Supplemental Figure 7. The cutting temperature rises as the cutting speed increases as in 80-260 m/min.…”

“…In this context, the precision hard turning high precision components is considered a cost-effective substitute to traditional grinding process. 18 Low friction of coefficient of tool coating is used to minimize the tool wear mechanism and enhance surface quality. 19 Tool coatings have provided a low friction coefficient, high hardness, and thermal stability to the tool.…”

Investigation of the effect of vibration in the multi-objective optimization of dry turning of hardened steel

“…In this study, a fuzzy TOPSIS-based Taguchi method was utilized to determine the optimum cutting parameters level for machining hardened steel at dry conditions using the ceramic tool. The productivity of the machining process solely depends on controlling the response parameters, and it is quite challenging due to the interaction between the responses (Abbas et al. , 2022).…”

“…In this study, a fuzzy TOPSIS-based Taguchi method was utilized to determine the optimum cutting parameters level for machining hardened steel at dry conditions using the MOO of dry turning of hardened steel ceramic tool. The productivity of the machining process solely depends on controlling the response parameters, and it is quite challenging due to the interaction between the responses (Abbas et al, 2022). Since this study seeks to optimize the responses Main effects plot for signal to noise ratio for X-direction peak amplitude IJIEOM 5,1 simultaneously, the optimal conditions obtained will facilitate the dry hard-turning of steels by minimizing Ra, cutting force and cutting power consumption while maximizing the material removal rate.…”

“…Sharma et al (2021) compared the Taguchi-Grey approach, teaching learning-based optimization (TLBO) and a grey regression for MOO of stainless steel 316L alloy under dry cutting conditions, where grey theory showed better performance. Abbas et al (2022) utilized a multi-objective emperor penguin colony algorithm (MOEPCA), multi-objective Pareto search algorithm (MOPSA) and multi-objective genetic algorithm (MOGA) in the MOO of Ra and productivity during the hard-turning of AISI 4340 steel.…”