Effect of magneto rheological minimum quantity lubrication on machinability, wettability and tribological behavior in turning of Monel K500 alloy

Kulandaivel, Arul; Kumar, Sriram

doi:10.1080/10910344.2020.1765179

Cited by 47 publications

(8 citation statements)

References 44 publications

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“…e turning experimental trails are done using central lathe, and cubic boron nitride tools are utilized. Taguchi is used to plan the experiment and L 27 array is used to do experimental trails [11][12][13][14]. e surface roughness was determined using surf-coder profilometer and an average of three measurements was taken at every machining condition.…”

Section: Materials and Experimental Detailsmentioning

confidence: 99%

“…Moreover, the SVM and GA techniques have produced accurate prediction and optimization of the parameters. Machine learning technologies are recently used widely to predict the attributes before the actual experiment as well as these techniques are widely used for the measurements of the outputs [10].It is also to be investigated for the best solution for optimum of outputs to reduce the wastage of material and cost of machining in machining [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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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

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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.

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Section: Materials and Experimental Detailsmentioning

confidence: 99%

Section: Introductionmentioning

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

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“…e average of the three Several factors affect the production rate in the manufacturing industry. e major factors such as cutting speed, feed rate, and depth of cut are the predominant process parameters in any machining industry [21]. is study provides the influence of tool coating on the output responses such as Torque, thrust force, and Material Removal Rate.…”

Section: Experimental Details and Data Analysismentioning

confidence: 99%

Statistical Modelling to Study the Implications of Coated Tools for Machining AA 2014 Using Grey Taguchi‐Based Response Surface Methodology

Manoharan

Kulandaivel²,

Arunagiri

et al. 2021

Advances in Materials Science and Engineering

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Milling is the surface machining process by removing material from the raw stock using revolving cutters. This process accounts for a major stake in most of the Original Equipment Manufacturing (OEM) industries. This paper discusses optimizing process parameters for machining the AA 2014 T 651 using a vertical milling machine with coated cutting tools. The process parameters such as cutting speed, depth of cut, and type of the cutting tool with all its levels are identified from the previous literature study and several trial experiments. The Taguchi L9 Orthogonal Array (OA) is used for the experimental order with the chosen input parameters. The commonly used cutting tools in the machining industry, such as High-Speed Steel (HSS) and its coated tools, are considered in this study. These tools are coated with Titanium Nitride (TiN) and Titanium Aluminum Nitride (TiAlN) by Physical Vapor Deposition (PVD) technique. The output responses such as cutting forces along the three-axis are measured using a milling tool dynamometer for the corresponding input factors. The input process parameters are optimized by considering the output responses such as MRR, machining torque, and thrust force. Grey Taguchi-based Response Surface Methodology (GTRSM) is used for multiobjective multiresponse optimization problems to find the optimum input process parameter combination for the desired response. Polynomial regression equations are generated to understand the mathematical relation between the input factor and output responses as well as Grey Relational Grade (GRG) values. The optimum process parameter combination from the desirability analysis is the HSS tool coated with TiAlN at a cutting speed of 270 rpm and a depth of cut value of 0.2 mm.

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“…With the effects of friction between the workpiece and work tool taken into consideration, various lubricants have been selected during machining. These lubricants contain properties making them suitable to act as coolants and anti-wear agents [12][13]. Some include soluble oils, synthetic oils, semi-synthetic oils, and straight oils used for machining [14].…”

Section: Introductionmentioning

confidence: 99%

Experimental Study and Finite Element Analysis of Temperature Reduction and Distribution During Machining of Al-Si-Mg Composite Using Deform 3D

Okokpujie

Akujieze

Sinebe

et al. 2022

ARFMTS

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Composite materials are promising materials in the manufacturing industry due to the quality of their materials. However, in transforming these materials, the machining process experiences a high-heat generation rate, which has led to the study of temperature distribution, and reduction analysis at the cutting region. High-temperature generation during machining operation leads to thermal deformation on the developed component or parts, affecting the operation life span of the component. Thus, this study investigated the effect of mineral oil-based-Multi-walled carbon nanofluid (MWCNTs) compared to pure mineral oil in the turning of aluminum-silicon magnesium metal composite (AlSiMg) on temperature reduction and distribution. The nanofluid was prepared with 0.4g of MWCNT to 1 liter of mineral oil. The study employed the energy dispersive spectrometer to obtain the chemical composition of the developed nanofluid. Furthermore, Finite element software DEFORM 3D v11.0 uses a lagrangian incremental approach to simulate chip formation and temperature distribution on the workpiece. Also, to study the effects of the machining parameters on the temperature distribution. The experiment results showed a significant reduction of 11.9% in temperature when machining with nanofluid compared to pure mineral oil. The simulation results showed that the temperature increases as the cutting speed and feed rate increase. The minimum temperature via the DEFORM 3D Finite Element Model simulation was achieved at spindle speed 870 rpm, feed rate 2 mm/rev, and depth-of-cut 1 mm. In conclusion, the study recommends that the manufacturing industry employ the optimized machining parameters during the turning of AlSiMg metal matrix composite for a sustainable machining process.

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Effect of magneto rheological minimum quantity lubrication on machinability, wettability and tribological behavior in turning of Monel K500 alloy

Cited by 47 publications

References 44 publications

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

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

Statistical Modelling to Study the Implications of Coated Tools for Machining AA 2014 Using Grey Taguchi‐Based Response Surface Methodology

Experimental Study and Finite Element Analysis of Temperature Reduction and Distribution During Machining of Al-Si-Mg Composite Using Deform 3D

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