Multi-objective optimization of high-speed turning parameters for hardened AISI S7 tool steel using grey relational analysis

Abstract

Nowadays, die manufacturing industries prefer eco-friendly machining, i.e., high-speed turning for hardened AISI S7 tool steel followed by the conventional grinding process. The effectiveness of this eco-friendly turning depends on selection of appropriate process parameters, which decides the surface integrity of machined components. Hence, the first objective of present research work is to optimize the turning parameters for lower cutting force, machining temperature, surface roughness and higher material removal rate simultaneously using grey relational analysis (GRA). ANOVA utilized to identify the significant effect of each turning parameter on the response variables. Secondly, the effects of turning parameters such as tool nose radius (R n), cutting speed (V c), feed rate (V f) and depth of cut (d c) on tool wear and finished surface topography were studied through scanning electron microscopy (SEM) and atomic force microscope (AFM). Optimal turning parameters for multi-performance responses were R n : 1.2 mm, V c : 450 m/min, V f : 0.05 mm/rev and d c : 0.2 mm. The confirmation test was conducted on the optimal parameter level. According to ANOVA, depth of cut was the major influencing factor on all response variables. AFM and SEM micrograph indicated that excellent surface quality with lower surface roughness (S a : 64.21 nm and S q : 90.34 nm) was observed at higher cutting speed. Flank and crater wear were observed in cutting tool faces owing to thermo-mechanical loading. Different wear mechanisms like abrasion, adhesion, built-up edge formation and chipping hammering were found in the alumina-mixed ceramic insert at higher cutting speed and depth of cut.

Modeling and optimization of dead metal zone to reduce cutting forces in micro-milling of hardened AISI D2 steel

“…The SS estimates squared difference between each sample and its mean of all samples for each factor; MS estimates the ratio of factor spread deviation of SS to degree of freedom (DF); F-value is the ration of MS of factor to MS error; P value indicates significant parameters at 95% of confidence level (the Pvalue should be < 0.05). The factors, which are having Pvalue < 0.05 and F-value > 4 are said to be significant [29].…”

Machining-Reliability-based Optimization of Process Parameters for Marine Diesel Engine Block Hole System Using HJ-PSO

“…Step1: Given the initial point x (1) ∈ n , initial step size δ, acceleration factor α ≥ 1, reduction rate β∈(0,1), accuracy ε>0. Let y (1) =x (1) , k=1, j=1;…”

“…It is a common method to optimize the process parameters by exploring the relationship between the process parameters and the optimization objective through the experimental design. Awale et al [1] researched the influence of high-speed turning parameters on the surface roughness of harden AISI S7 tool steel by signal-to-noise ratio analysis method, and the results showed that higher cutting speed and lower feed rate can significantly improve the surface quality of hardening AISI S7 tool steel. Campatelli et al [2] conducted milling experiments on AISI 1050 carbon steel workpiece by NMV1500DC five-axis milling machine, and the process parameters with the lowest environmental footprint were obtained based on the response surface method, which are higher cutting speed, feed rate, and chip section.…”

Optimization of compressed air assisted-turning-burnishing process for improving machining quality, energy reduction and cost-effectiveness

“…Laouissi et al (2019) proposed the solutions to predict the tangential cutting force (Fz), cutting power (Pc), the MRR, and SR for the turning process of the cast iron, based on the ANN and RSM models [7]; and the authors emphasized that the ANN model could be applied to provide higher precision, as compared to the RSM one. Awale et al (2020) applied the GRA model to obtain the improvements in the machining force (Fc), machining temperature (MT), SR, and MRR [8]; and the outcomes indicated that the optimal values of the r, v, f, and a were 1.2 mm, 450 m/min, 0.05 mm/rev, and 0.2 mm, respectively. Nguyen et al (2020) applied the adaptive neuro-fuzzy inference system (ANFIS) to show the relations between the machining rate (MR), EC, and Ra in terms of the inclination angle (α), a, f, and v for the rotary turning [9], in which the adaptive simulated annealing (ASA) was used to select the optimal outcomes; and the authors stated that the EC and Ra were reduced by 50.29% and 19.77%, while the MR was improved by 33.16%, respectively.…”

“…The authors emphasized that the ANN model could be applied to provide higher precision, as compared to the RSM one. Awale and Inamdar 8 applied the GRA model to obtain the improvements in the machining force (F c ), machining temperature (MT), SR, and MRR. The outcomes indicated that the optimal values of the r, V, f , and a were 1.2 mm, 450 m/min, 0.05 mm/rev, and 0.2 mm, respectively.…”