2020
DOI: 10.1016/j.matpr.2020.03.163
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Effect of Tool Nose Radius and Machining Parameters on Cutting Force, Cutting Temperature and Surface Roughness – An Experimental Study of Ti-6Al-4V (ELI)

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Cited by 23 publications
(15 citation statements)
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“…ANOVA test was conducted to assess contribution of parameters. Developed model was interfaced with particle swarm optimization to minimize responses (Shah and Bhavsar 2020). To understand impacts of cutting edge microgeometries on surface integrity, experimental were conducted for a varied range of cutting tool geometries and feeds.…”
Section: Literature Reviewmentioning
confidence: 99%
“…ANOVA test was conducted to assess contribution of parameters. Developed model was interfaced with particle swarm optimization to minimize responses (Shah and Bhavsar 2020). To understand impacts of cutting edge microgeometries on surface integrity, experimental were conducted for a varied range of cutting tool geometries and feeds.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The levels of cutting parameters are selected based on previous literature and tool manufacturer recommendation as cutting speed (v) of 70-140-210 m/min, feed (f) of 0.1–0.15–0.2 mm/rev and depth of cut (d) of 0.1–0.2–0.3 mm, respectively. 21,22,24,25 The MQL setup (DROPSA, Italy) was utilized to deliver the LRT 30 iron-aluminium mineral oil into the cutting zone. The LRT 30 oil hols good anti-corrosion characteristics and it is beneficial for the machining environment concern.…”
Section: Scheme Of Experimentationmentioning
confidence: 99%
“…Singh et al 23 observed that 1.5% weight concentration of graphene in canola oil–based MQL outperformed over conventional synthetic fluid during surface grinding of Ti-6Al-4V ELI alloy for tribological application. Shah and Bhavsar 24 studied the effect of nose radius and cutting parameters on cutting temperature, surface roughness and cutting force during turning Ti-6Al-4V ELI alloy. Mathematical models are developed and optimized through particle swarm optimization (PSO) to minimize the responses.…”
Section: Introductionmentioning
confidence: 99%
“…Shi [10] and Qu [11] established a mathematical model of cutting temperature and cutting force to prove the correlation between cutting temperature and cutting parameters. Shah et al [12] studied the influence of cutting parameters on the cutting temperature of Ti-6Al-4V cutting process through particle swarm optimization algorithm. Turkes et al [13] used the finite element analysis to calculate the cutting parameters, and found that the temperature distribution of cutting tools and processing materials through the parameters.…”
Section: Introductionmentioning
confidence: 99%