Experimental investigation and optimization of cutting parameters with multi response characteristics in MQL turning of AISI 4340 using nano fluid

Patole, P.B.; Kulkarni, Vivek

doi:10.1080/23311916.2017.1303956

Cited by 24 publications

(13 citation statements)

References 9 publications

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“…Agustina et al [ 17 ] observed that for the aluminum alloy A97075, the most significant cutting parameter is feed rate, followed by the depth of cut and spindle speed. Similarly, studies related to the turning of other materials such as AISI 4340 steel [ 41 ], AISI 4140 steel [ 42 ], red brass [ 43 ], Inconel 718 [ 44 ], also showed that feed rate and depth of cut have a significant effect on cutting forces while cutting speed is found to be insignificant.…”

Section: Resultsmentioning

confidence: 99%

Experimental Investigation and Statistical Evaluation of Optimized Cutting Process Parameters and Cutting Conditions to Minimize Cutting Forces and Shape Deviations in Al6026-T9

et al. 2020

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Precise, economical and sustainable cutting operations are highly desirable in the advanced manufacturing environment. For this aim, the present study investigated the influence of cutting parameters (i.e., the cutting speed (c), feed rate (f), depth of cut (d) and positive rake angle (p)) and sustainable cutting conditions (dry and minimum quantity lubricant (MQL)) on cutting forces (i.e., feed force (Ff), tangential forces (Ft), radial force (Fr) and resultant cutting forces (Fc) and shape deviations (i.e., circularity and cylindricity) of a 6026-T9 aluminum alloy. The type of lubricant and insert used are virgin olive oil and uncoated tungsten carbide tool. Turning experiments were performed on a TAKISAWA TC-1 CNC lathe machine and cutting forces were measured with the help of a Kistler 9257B dynamometer. Shape deviations were evaluated by means of a Tesa Micro-Hite 3D DCC 474 coordinate measuring machine (CMM). Experimental runs were planned based on Taguchi mixture orthogonal array design L16. Analysis of variance (ANOVA) was performed to study the statistical significance of cutting parameters. Taguchi based signal to noise (S/N) ratios are applied for optimization of single response, while for optimization of multiple responses Taguchi based signal to noise (S/N) ratios coupled with multi-objective optimization on the basis of ratio analysis (MOORA) and criteria importance through inter-criteria correlation (CRITIC) are employed. ANOVA results revealed that feed rate, followed by a depth of cut, are the most influencing and contributing factors for all components of cutting forces (Ff, Ft, Fr, and Fc) and shape deviations (circularity and cylindricity). The optimized cutting parameters obtained for multi responses are c = 600 m/min, f = 0.1 mm/rev, d = 1 mm and p = 25°, while for cutting conditions, MQL is optimal.

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Section: Resultsmentioning

confidence: 99%

Experimental Investigation and Statistical Evaluation of Optimized Cutting Process Parameters and Cutting Conditions to Minimize Cutting Forces and Shape Deviations in Al6026-T9

et al. 2020

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“…As discussed in [21], the uncut chip would be thicker as an increasing feed rate, resulting in higher cutting forces and vibration. On the other hand, Mohd Asyraf Mahboob Ali et al [11] pointed out that the increase of nanoparticle concentration strengthened the thin protective fi lm on the machined surface so the surface could be improved. It was also discussed that the low friction behavior of nanoparticles contributed to minimizing the frictional effects on tool-workpiece, hence reducing tool wear and cutting force.…”

Section: Resultsmentioning

confidence: 99%

“…Different types of nanoparticles were used by researchers including SiO 2 , Al 2 O 3 , MoS 2 , ZrO 2 , CNT, SiC, PCD, and graphite [9,10] while others are under investigation. The effectiveness of nanoparticles has been reported in turning [11][12][13], milling [14,15], drilling [16] for better surface quality, prolonged tool life, reducing cutting force, reducing tool wear, and reducing power & energy consumption. Khaled Ali Osman et al [17] performed slot milling of Ti6Al4V under various concentrations of hBN nanoparticles in order to optimize surface quality and cutting forces.…”

Section: Introductionmentioning

confidence: 99%

An empirical investigation of SIO2 nano concentration under MQL on surface roughness in hard milling of jis SKD61 steel

Tam¹,

Phan²

2020

J Appl Eng Science

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Surface roughness is an important assessment of metal cutting. This paper presents an empirical investigation of cutting conditions on the surface roughness in hard milling SKD61 steel. The cutting speed, feed rate, depth of cut, and nanoparticle concentration were taken as the parameters in the experimental setup. The mixer of SiO 2 particles with a size of 100nm based on cutting oil CT232 was used with 3 levels of concentration: 0, 2, and 4wt%. Twenty-seven experiments were carried out based on the DOE method developed by G. Taguchi. The best model from response surface methodology (RSM) was developed regarding the surface roughness. Further analysis with ANOVA method was performed to confi rm the signifi cant of the achieved model as well as machining parameters. According to experiment results, the weight percent of nanoparticles concentration had a great impact on the surface roughness, only after the feed rate. Additionally, the excellent effectiveness in reducing the roughness of MQL nanofl uid has been demonstrated when compared with conventional MQL.

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“…In order to perform the experiment in MQL cutting environment ethylene glycol as a base fluid with Multiwalled carbon nano tube (MWCNT) nano particles has been used as a cutting fluid. The cutting fluid is prepared with mixture of ethylene glycol and the MWCNT in the concentration of 0.2% [27]. According to supplying direction and distance of the cutting fluid, in this experiment the distance between nozzle and insert tip was fixed at 11 mm and cutting fluid were supplied at a 30°a ngle [26].…”

Section: Methodsmentioning

confidence: 99%

“…The general purpose of MWCNT is improvement in cutting performance because it is having higher heat carrying capacity which improves cooling effect and results in better surface finish [7]. During the experimentation MQL system supplied ethylene with nano particle at pressure 5 bar and flow rate140 mL/h [27]. The work piece material used is a cylindrical bar of AISI 4340 having BHN 217, 100 mm length and 24 mm diameter.…”

Section: Methodsmentioning

confidence: 99%

Prediction of surface roughness and cutting force under MQL turning of AISI 4340 with nano fluid by using response surface methodology

Patole

Kulkarni

2018

Manufacturing Rev.

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This paper presents an investigation into the minimum quantity lubrication mode with nano fluid during turning of alloy steel AISI 4340 work piece material with the objective of experimental model in order to predict surface roughness and cutting force and analyze effect of process parameters on machinability. Full factorial design matrix was used for experimental plan. According to design of experiment surface roughness and cutting force were measured. The relationship between the response variables and the process parameters is determined through the response surface methodology, using a quadratic regression model. Results show how much surface roughness is mainly influenced by feed rate and cutting speed. The depth of cut exhibits maximum influence on cutting force components as compared to the feed rate and cutting speed. The values predicted from the model and experimental values are very close to each other.

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Experimental investigation and optimization of cutting parameters with multi response characteristics in MQL turning of AISI 4340 using nano fluid

Cited by 24 publications

References 9 publications

Experimental Investigation and Statistical Evaluation of Optimized Cutting Process Parameters and Cutting Conditions to Minimize Cutting Forces and Shape Deviations in Al6026-T9

Experimental Investigation and Statistical Evaluation of Optimized Cutting Process Parameters and Cutting Conditions to Minimize Cutting Forces and Shape Deviations in Al6026-T9

An empirical investigation of SIO2 nano concentration under MQL on surface roughness in hard milling of jis SKD61 steel

Prediction of surface roughness and cutting force under MQL turning of AISI 4340 with nano fluid by using response surface methodology

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