Investigation of parameters and morphology of coated WC tool while machining X-750 using NSGA-II

Abstract

Nickel-based superalloys have numerous applications in automobile, aerospace, turbine blades, nuclear, oil refinery etc., due to their excellent properties like strength, wear resistance, corrosion resistance and higher creep strength. Because of these properties, modern manufacturing industries need help with the machining of nickel-based superalloys, i.e. hard-to-machine materials. In the present research, Ni-based X-750 alloy is machined with turning operation by a conventional lathe machine using a TiAlN PVD coated tungsten carbide tool at different rotational speed (TRS), depth of cut (DoC) and feed (F) values as input parameters whereas material removal rate (MRR) and tool wear (TW) are the responses of the study. The design of experiments (DoE) is prepared by response surface methodology-based Box-Behnken Design. Analysis of variance (ANOVA) was applied to investigate the percentage contribution of each machining parameter on responses. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) simultaneously optimizes the developed empirical models of MRR and TW. The predicted solutions suggested by NSGA-II are the best solution, and confirmation experiments are conducted on randomly selected parametric settings from these solutions. The optimized set presented by NSGA-II is TRS: 900RPM; DoC: 0.06mm; F: 0.1mm/rev, and the maximum relative error in the case of MRR and TW is in the permissible limit. Scanning electron microscopy (SEM) and Energy dispersive spectroscopy (EDS) are used to investigate the morphology of tool insert before and after machining at optimized value TRS: 900 RPM; DoC: 0.1 mm; F: 0.06 mm/rev, and it shows the wear marks on the tool, and the Energy dispersive spectroscopy confirms the presence of coating and WC. SEM is used to investigate the morphology of chips formed at different optimized parametric settings.

Assessment of performance parameters in intermittent turning and multi-response optimization of machining conditions using DF, MOORA, VIKOR, and coupled NSGAII-VIKOR methods

“…Table (22) summarizes the optimal conditions proposed by the four (04) applied optimization methods, namely: (DF), (MOORA), (VIKOR), and NSGA-II / VIKOR in intermittent turning of AISI D3 steel, along with the corresponding optimized responses. The analysis of the results reveals that the optimal condition (r = 1.29 mm, Vc = 240 m/min, f = 0.1 mm/rev, ap = 0.689 mm) derived from the (DF) approach yields minimized optimized responses for all six (06) output parameters (Ra, Pm, VB, Ct, Fz, and Lp) with values of (0.825 µm, 3282.085 Watt, 0.066 mm, 211.683 °C, 82.837 N, 108.158 dB) respectively.…”

Optimizing machining efficiency: a comprehensive study on PVD cathodic arc evaporation coated turning tool inserts with TiAlN/AlCrN multilayer coatings

“…It is required to ascertain the physical, mechanical, chemical, and metallurgical (microstructure) qualities after performing the machining tests [36,37]. Figure 9 illustrates the uncoated and coated inserts' surface morphology by using Scanning Electron Microscope (SEM).…”

Multi-Objective Optimization of AISI P20 Mold Steel Machining in Dry Conditions Using Machine Learning—TOPSIS Approach

“…Previous research has detailed the machining of different type of steels and parametric optimization [22][23][24][25][26][27][28]. However, it is evident from the literature that limited work has been published on the milling of P20 mold steel.…”