Comparative Performance Analysis of Coated Carbide Insert in Turning of Ti-6Al-4V ELI Grade Alloy under Dry, Minimum Quantity Lubrication and Spray Impingement Cooling Environments

Anurag, .; Kumar, Ramanuj; Sahoo, Ashok Kumar; Panda, Amlana

doi:10.1007/s11665-021-06183-4

Cited by 15 publications

(3 citation statements)

References 54 publications

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“…The surface roughness was traced to be higher in magnitude when the feed rate was the highest (0.2 mm/rev). Also, Ra increased due to a gain in feed rate [27]. With an increase in feed rate, the volume of material increases, and material buildup in the form of chips occurs across the tool-work-chip interface.…”

Section: Assessment Of Surface Roughnessmentioning

confidence: 99%

WASPAS Based Multi Response Optimization in Hard Turning of AISI 52100 Steel under ZnO Nanofluid Assisted Dual Nozzle Pulse-MQL Environment

Khatai,

Kumar,

Panda

et al. 2023

Applied Sciences

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Hard turning is an emerging machining technology that evolved as a substitute for grinding in the production of precision parts from hardened steel. It offers advantages such as reduced cycle times, lower costs, and environmental benefits over grinding. Hard turning is stated to be difficult because of the high hardness of the workpiece material, which causes higher tool wear, cutting temperature, surface roughness, and cutting force. In this work, a dual-nozzle minimum quantity lubrication (MQL) system’s performance assessment of ZnO nano-cutting fluid in the hard turning of AISI 52100 bearing steel is examined. The objective is to evaluate the ZnO nano-cutting fluid’s impacts on flank wear, surface roughness, cutting temperature, cutting power consumption, and cutting noise. The tool flank wear was traced to be very low (0.027 mm to 0.095 mm) as per the hard turning concern. Additionally, the data acquired are statistically analyzed using main effects plots, interaction plots, and analysis of variance (ANOVA). Moreover, a novel Weighted Aggregated Sum Product Assessment (WASPAS) optimization tool was implemented to select the optimal combination of input parameters. The following optimal input variables were found: depth of cut = 0.3 mm, feed = 0.05 mm/rev, cutting speed = 210 m/min, and flow rate = 50 mL/hr.

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Section: Assessment Of Surface Roughnessmentioning

confidence: 99%

WASPAS Based Multi Response Optimization in Hard Turning of AISI 52100 Steel under ZnO Nanofluid Assisted Dual Nozzle Pulse-MQL Environment

Khatai,

Kumar,

Panda

et al. 2023

Applied Sciences

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“…Surface roughness is an important index to measure surface quality, and the surface roughness varies greatly under different process conditions. Anurag et al [11] studied the influence law of process parameters on surface roughness under dry cutting, minimum quantity lubrication and spray impingement cooling conditions. The research results show that there is little difference in surface quality between minimum quantity lubrication and spray impingement cooling conditions, but it is more powerful than dry cutting to improve surface quality.…”

Section: Introductionmentioning

confidence: 99%

“…Where m sa is the mean value of S , a m sa * is the mean value of S . a * According to equations(11) and(12) …”

mentioning

confidence: 99%

Research on surface roughness prediction in turning Inconel 718 based on Gaussian process regression

Hao¹,

Cheng²,

Fan³

2022

Phys. Scr.

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Nickel-based alloy Inconel 718 is widely used in aircraft engine industry because of its good mechanical properties. Inconel 718 is a typical difficult-to-machine material and its price is relatively expensive. Therefore, accurate prediction of Inconel 718 machined surface roughness with small sample space can improve machining efficiency, optimize process parameters and reduce machining cost. In this paper, a method is proposed to characterize the influence of cutting parameters on roughness by stablishing the corresponding relationship between the proportional hyperparameters in the multivariate kernel function and the cutting speed, cutting deep, feed rate and the rake angle of the tool. A multi input single output (MISO) multivariate Gaussian process regression（GPR）surface roughness prediction model with cutting speed, cutting depth, feed rate and tool rake angle as input variables and surface roughness as output variables is established. The model can not only output the predicted value of surface roughness, but also give the reliability of the predicted value. Experimental results show that the proportional hyperparameter has an independent adjustment function, and the influence of the process parameters characterized by the proportional hyperparameter on the surface roughness is consistent with the experimental results. The experimental results show that the average relative error of MISO multivariate GPR surface roughness prediction model proposed in this paper is 1.5%, which can accurately predict the surface roughness in small sample space.

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Optimization of the Turning Process by Means of Machine Learning Using Published Data

de Arriba-Pérez,

García-Méndez,

Carou

et al. 2024

Materials Forming, Machining and Tribology

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Comparative Performance Analysis of Coated Carbide Insert in Turning of Ti-6Al-4V ELI Grade Alloy under Dry, Minimum Quantity Lubrication and Spray Impingement Cooling Environments

Cited by 15 publications

References 54 publications

WASPAS Based Multi Response Optimization in Hard Turning of AISI 52100 Steel under ZnO Nanofluid Assisted Dual Nozzle Pulse-MQL Environment

WASPAS Based Multi Response Optimization in Hard Turning of AISI 52100 Steel under ZnO Nanofluid Assisted Dual Nozzle Pulse-MQL Environment

Research on surface roughness prediction in turning Inconel 718 based on Gaussian process regression

Optimization of the Turning Process by Means of Machine Learning Using Published Data

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