Development of Porous Ni40Ti50Cu10 Using Taguchi Approach

Singh, Gurpreet; Kumar, Arvind; Sharma, Neeraj

doi:10.47406/oer.2020.1103

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…The effects of control parameters i.e. abrasive, slurry concentration, type of material, feed rate and tool speed wear rate and MRR are evaluated by using ANOVA (Chaudhary et al, 2013;Sharma et al, 2012;Singh et al, 2020). Signal-tonoise characteristics are used to evaluate the effectiveness of a design.…”

Section: Resultsmentioning

confidence: 99%

Experimental Investigations on Tool Wear during Abrasive Assisted Drilling of Aluminum Matrix Composite Using ANOVA

Kumar¹,

Kumar²

2020

OER

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The effect of material removal rate of Aluminum Matrix Composite has been evaluated experimentally as well as analytically in terms of tool wear to investigate the performance of high-speed steel twist drills. Three different concentrations of 1200 microns fine mesh size of Silicon Carbide and Aluminum oxide abrasives are used in abrasive slurry system. Through several performance characteristics for material removal rate and tool wear, the drilling parameters such as spindle speed, feed rate, slurry concentration and mesh size are calibrated. Experiments are planned by application of Taguchi method and conducted on CNC vertical machining center. The software package MINITAB 16 is used for analytical result evaluation. The feed rate indicates the maximum effect and the concentration of slurry has a negligible effect on the results predicted by the analysis of variance. At the maximum feed rate, speed and slurry concentration, the signal-to-noise ratio displays the optimum performance. Scanning electron microscopy and Electron detector system show the tool wear rate at different speeds, feeds and slurry concentrations. The critical variables which affecting the tool wear and removal rate of material are the feed rate and work piece material.

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Section: Resultsmentioning

confidence: 99%

Experimental Investigations on Tool Wear during Abrasive Assisted Drilling of Aluminum Matrix Composite Using ANOVA

Kumar¹,

Kumar²

2020

OER

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“…Different researchers used the statistical techniques for the experimental design to investigate the predictive model of response characteristics (cutting cost, cutting forces, material removal rate, SR, tool wear, tool life) (Kataria and Kumar, 2014; Sharma et al , 2015; Gupta and Kumar, 2015; Jangra et al , 2016; Kataria et al , 2017; Jha et al , 2018; Singh et al , 2020; Gupta et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Multi-response optimization while machining of stainless steel 316L using intelligent approach of grey theory and grey-TLBO

Sharma

Дабра²,

Singh

et al. 2021

WJE

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Purpose Stainless steel is widely used in different manufacturing sectors. The purpose of this study is to optimize the process parameters of machining while processing SS316L alloy. The optimization of machining characteristics in the case of SS316L alloy greatly improves the quality and productivity economically. Design/methodology/approach The machining variables in current research are depth of cut, spindle speed and feed rate. The optimization of response characteristics was carried out using the intelligent approach of grey, regression and teaching learning-based optimization (TLBO) and Taguchi-Grey approach. Planning of experiments was made using Taguchi’s based L27 orthogonal array. With the implementation of grey, the response characteristics were normalized and converted into a single response. The regression analysis was used for empirical modeling of the single response induced from the grey application. TLBO is further used to investigate the combinations of machining variables and compared with grey theory. Findings The grey-TLBO based multi-criteria decision-making approach suggests that the optimized setting for material removal rate, mean roughness depth (Rz) and cutting force (Fz) is spindle speed (N): 720 rpm; feed rate (F): 0.3 mm/rev; depth of cut (DoC): 1.7 mm. The grey theory suggests an optimized setting as N: 720 rpm; F: 0.2 mm/rev and DoC: 1.7 mm. Originality/value The parametric optimization during the turning of SS316L using grey-TLBO based intelligent approach is not performed till now. Thus, this intelligent approach will give a path to the researchers working in this direction. However, the grey theory performs better as compared to the grey-TLBO approach.

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