Experimental Investigation on Turning of Turbine Blade Material AISI 410 under Minimum Quantity Cutting Fluid

Nune, Madan Mohan Reddy; Chaganti, Phaneendra Kiran

doi:10.1016/j.matpr.2017.01.120

Cited by 5 publications

(2 citation statements)

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“…Most commonly used nanoparticles in the base fluid were titanium oxide, molybdenum disulphide and silicon dioxide. Compared to others, SiO 2 nanoparticles have shown a significant improvement in machining and thermal properties and a reasonable improvement in lubrication effect [7][8][9][10][11]. These SiO 2 nanoparticles impinge between metal surfaces and create rolling action enhancing lubrication [12].…”

Section: Imentioning

confidence: 99%

“…The surface finish of these materials is critical as poor surface finish leads to surface cracks and to fail at high centrifugal forces [14]. There are a few studies in the literature on the machinability of AISI 420 using nanolubricants [8][9][10] and some of them used DOE (design of experiments) [15]. Frequently used DOE are response surface and Taguchi methods.…”

Section: Imentioning

confidence: 99%

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Investigating Optimum SiO2 Nanolubrication During Turning of AISI 420 SS

Reddy

Chaganti

2019

Eng. Technol. Appl. Sci. Res.

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AISI 420 martensitic stainless steel is used for making gas and steam turbine blades, steel balls and medical instruments, due to its anti-corrosive properties. Turning of AISI 420 SS would be a worthy procedure specifically in manufacturing high surface finish parts. In this work, effort has been made to investigate the cooling and lubricating performance of SiO2 (silicon dioxide) nanoparticles at different weight concentrations of 0.1g, 0.5g and 1g mixed in a novel developed base fluid (synthetic). The performance of optimum SiO2 based cutting fluid is evaluated based on the turning process with output responses like surface finish and cutting temperature. Taguchi technique was used with standard L9(3**4) orthogonal array. The responses, surface roughness, and cutting temperature were analyzed using S/N (signal-to-noise) and ANOVA (analysis of variance). This analysis identifies the significant input parameter combination to obtain minimum surface roughness and temperature.

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