Optimization of surface roughness and tool wear in sustainable dry turning of Iron based Nickel A286 alloy using Taguchi’s method

Ramana, M. Venkata; Rao, G Sudhakara; Sagar, Bidya; Panthangi, Ravi Kumar; Kumar, Bikash

doi:10.1016/j.clet.2020.100034

Cited by 15 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e increased cutting speed increases the average surface roughness for the uncoated cutting tool, while there is a marginal change for the coated tool. is behavior tallies with results presented by [48,49]. is is because the rapid deformation of the uncoated tools brings about an increase in surface roughness due to the material's deformation hardening property.…”

Section: S/n Ratio Analysissupporting

confidence: 86%

Optimization of Surface Quality and Power Consumption in Machining Hardened AISI 4340 Steel

Ochengo

Zhao

et al. 2022

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

Hard turning has become an attractive method of machining for most manufacturers in the last few years due to its low cost and superior surface quality compared to grinding. In this experimental study, the machinability of hardened steel under dry machining on a CNC lathe is undertaken to optimize the cutting parameters for minimum surface roughness and energy consumption with the cutting speed (320, 450, and 575), tool type (coated and uncoated), and feed rate (0.1, 0.18, and 0.26) as the input parameters. The Taguchi method, based on the L18 orthogonal array, the variance analysis, the signal-to-noise ratios, and the response surface methodology have been used to optimize surface roughness (Ra) and cutting power (Cp). Optimum cutting parameters and levels were determined, and the relationship between cutting parameters and output variables was analyzed with the aid of two-dimensional and three-dimensional graphics. The results show that the most effective parameter on the surface roughness was the tool type (78%), while the most effective parameter on energy consumption was the cutting speed (90%). The combination of low feed rate and high cutting speed is necessary for minimizing the surface roughness. Besides, the impact of two-factor interactions of the feed rate-cutting speed and depth of cut-cutting speed appears to be substantial. The linear regression models were validated using confirmation tests. Finally, regression coefficients were determined as a mathematical model, and it was observed that this estimated model yielded results that were very similar to those achieved via real experiment (correlation values: 97.64% for surface roughness and 98.72% for energy consumption).

show abstract

Section: S/n Ratio Analysissupporting

confidence: 86%

Optimization of Surface Quality and Power Consumption in Machining Hardened AISI 4340 Steel

Ochengo

Zhao

et al. 2022

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

show abstract

“…e insert tool life is calculated by measuring the insert flank wear at every 50 seconds once and the time period is noted at final insert worn out stage. e feed rate impacts mainly on these responses compare to other factors and it is accepted that, as the feed rate escalates the roughness, wear increases and life of the insert reduces [18,19].It is observed that the increases in roughness at trail 8 is equal to 3.384 times higher than the roughness observed at trail 20. Similarly, force and flank wear observed at trail 8 is equal to 1.63 and 1.21 times higher than the trail 20, respectively.…”

Section: Resultsmentioning

confidence: 99%

Modelling and Analysis of Surface Roughness Using the Cascade Forward Neural Network (CFNN) in Turning of Inconel 625

Rajesh

Prabhuswamy

Raghavan

2022

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

In this paper, the influence of process components on surface roughness in turning of Inconel 625 using cubic boron nitride (CBN) is studied. A predictive model is developed to forecast the surface roughness using the cascade forward neural network (CFNN). The experiments are designed based on Taguchi. L27 orthogonal array (OA) is used to perform the experimental trails by considering speed, feed, and depth of cut as input factors. Out of 27 experimental trails, 18 experiments are used for training and 9 experimental trails are used for testing. The developed predictive model by the CFNN is compared with regression model values. The average prediction error for surface roughness is 2.94% with R2 = 99.99% by the CFNN. The CFNN is known to be superior to predict the response with minimum of percentage error. The minimum and maximum roughness observed at trail 8 and trail 20 is noted, respectively, and the increases in roughness at experimental trail 8 is equal to 3.384 times higher than the roughness observed at experimental trail.20. The feed rate dominates effectively on the roughness rather than other factors. The consequences of process factors on surface roughness are studied with the help of ANOVA. This experimental study and developed model would be used for aero parts manufacturing to forecast the roughness accurately before to the actual experiment so that actual machining and material cost could be avoided.

show abstract

“…TiAlN/TiN coated cemented carbide tool is ideal for machining nickel-base superalloy because of its low friction coefficient and hardness [2][3][4]. However, the accumulation of cutting heat will cause the softening and plastic deformation of the coating, which will lead to damage to the coated tool [5,6].…”

Section: Introductionmentioning

confidence: 99%

First-principles study of V-doped TiAlN and TiN coating materials

Yang

Yue

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

In this paper, the unit cell model of V-doped TiN and TiAlN is constructed, and the formation enthalpy of the unit cell and lattice parameters of the doped models are calculated by first principles. The results show that the lattice parameters of TiN and TiAlN unit cells are reduced, and the structure of the unit cell is stable after V is doped into TiN and TiAlN unit cells. The structural stability of the TiN unit cell increases with V doping into the TiN unit cell, while that of the TiAlN unit cell decreases with the increase of V doping into the TiAlN unit cell. The analysis results of unit cell hardness and Mulliken population show that the hardness of TiN and TiAlN coating materials is improved by doping V and that hardness increases with the increase of V doping. The reason is that V doping into TiN and TiAlN unit cells increases their average bond population. The covalent bonding property of the unit cell is increased, and the interatomic bonding strength of the unit cell is increased, which leads to an increase in the unit cell’s hardness.

show abstract

Optimization of surface roughness and tool wear in sustainable dry turning of Iron based Nickel A286 alloy using Taguchi’s method

Cited by 15 publications

References 11 publications

Optimization of Surface Quality and Power Consumption in Machining Hardened AISI 4340 Steel

Optimization of Surface Quality and Power Consumption in Machining Hardened AISI 4340 Steel

Modelling and Analysis of Surface Roughness Using the Cascade Forward Neural Network (CFNN) in Turning of Inconel 625

First-principles study of V-doped TiAlN and TiN coating materials

Contact Info

Product

Resources

About