A Numerical Model to Obtain Temperature Distribution During Hard Turning of AISI 52100 Steel

A Numerical Model to Obtain Temperature Distribution During Hard Turning of AISI 52100 Steel

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ScienceDirect Materials Today: Proceedings 2 (2015) 1907 – 1914

4th International Conference on Materials Processing and Characterization

A Numerical Model to Obtain Temperature Distribution during Hard Turning of AISI 52100 Steel P.S. Bapata,*, P.D. Dhikalea, S.M. Shindea, A.P. Kulkarnib, S.S. Chinchanikarb a

U.G. Student, Department of Mechanical Engineering, Vishwakarma Institute of Information Technology, Pune 411048, INDIA b Professor,Department of Mechanical Engineering, Vishwakarma Institute of Information Technology, Pune 411048, INDIA

Abstract Temperature is of interest in machining because cutting tools often fail by thermal softening or temperature-activated type of wear. In this study, a numerical model was developed to obtain temperature distribution in hardturningof AISI 52100 steel. Temperature distribution model as a function of heat generation was developed using ABAQUS explicit and with an Arbitrary Lagrangian-Eulerian (ALE) formulation approach. The heat generation in the primary and the secondary stress deformation zone and along the sliding stress frictional zone at the tool–chip interface was introduced while developing a model. Johnson cook plastic flow material model was used to model the work piece material properties. A series of thermal simulations were carried out to obtain the value and region of maximum temperature at various cutting conditions. It has been observed that cutting temperature increases with the increase in cutting speed. The simulated results of the temperature distribution showed a good agreement with the results available in the literature and hence, the model developed could be used to predict the temperature distribution duringhard turning of AISI 52100 steel. © 2014Elsevier The Authors. Ltd. All rights reserved. © 2015 Ltd. AllElsevier rights reserved. the 4th International Selection andpeer-review peer-review under responsibility the conference committee Selection and under responsibility of theofconference committee membersmembers of the 4thofInternational conference conference on Materials on Materials and Characterization. Processing Processing and Characterization. Keywords:Hard turning; Heat generation; Finite element method; Temperature distribution

Hard turning as it is widely used now a days, the understanding of the material removal considering the effect of work material, tool material and cutting conditions on the machining performance is very essentialto ensure the

* Corresponding author. Tel.: +020-26932300; fax: +020-26932500. E-mail address: [email protected]

2214-7853 © 2015 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the conference committee members of the 4th International conference on Materials Processing and Characterization. doi:10.1016/j.matpr.2015.07.150

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quality of the products [1-4]. Although, metal cutting tests have been carried out for the past 150 years on a large scale,it sometimes referred to as one of the least understood manufacturing processes due to the complexities associated with the process. A lot of investigations and modeling attempts have been made by the researchers to optimize and to study the effects of cutting parameters on various performance measures such as cutting forces, cutting temperature, tool wear during machining of hardened steel [5-8]. Detailed works to determine cutting temperature using analytical approach began in 1951 by Hahn [4-5] followed by Chao and Trigger [7], Leone [8] and Rapier [9]. However there are many phenomenon that are not easily observed or not subjected to direct experimentation. Most of the researchers used Finite Element Analysis(FEA) to simulate the metal cutting processes to obtainthe cutting temperature, cutting forces, residual stressesand to understand the chip formation processes [10-11].The FEA could be helpful to obtain information about the material flow and the stress, stain and temperature distribution to optimize the machining process. FEA involve simulation of short-time large deformation problems which require the use of either implicit or explicit solution techniques. Implicit methods deal with mostly non-linear system with large steps whereas explicit method deals with highly non-linear system with small steps. Therefore, the implicit solution integration methods take a longer time for simulation for fast dynamic process calculation. Therefore, processes such as material removal are calculated using explicit integration method[12]. Machining process usually treated as a coupled thermo mechanical process as during machining all the mechanical energy is converted into heat. Major part of the heat is generated due to severe plastic deformation in a very narrow zone called primary stress deformation zone (PSDZ). Further heating is generated in the secondary stress deformation zone (SSDZ) due to friction between tool and chip as well as shearing at the chip-tool interface. It is reported that PSDZ and SSDZ zones together account for almost 99% of the total energy which is converted into heat in the cutting process [13]. Heat generated during machining processat the Primary Shear Deformation Zone (PSDZ), Secondary Shear Deformation Zone (SSDZ) and Tertiary Shear Deformation Zone (TSDZ) (Fig. 1) increases the cutting temperature, which has a detrimental effect on the machining performance.

Fig. 1. Shear deformation zone.

Numerical modeling of machining process was attempted by researchers using Lagrangian-based formulation as it is easy to implement and computationally efficient [14]. In this formulation nodes are coincident with material points. However, elements get highly distorted as the mesh deforms with the deformation of the material in front of the tool tip. Attempts also have been made by the researchers using Eulerian-based formulation wherein nodes are coincident with spatial points[11, 15]. Each method discussed above has its own advantages and some limitations. Arbitrary Lagrangian and Eulerian Formulation (ALE) which was mostly attempted by the researchers to formulate the numerical model of machining process combines the advantages of both the methods [16-17]. In this formulation, the boundary nodes are moved to remain on the material boundaries, while the interior nodes are moved to minimize mesh distortion.Puri et al [18] reviewed models and techniques for predicting the temperature distributionsin heat affected zone. In this paper, attempt has been made to develop a fully coupled thermo mechanical finite element modelto obtain the temperature distribution during hard turning of AISI 52100 steel. Explicit dynamic ALE formulation which is known to be very efficient for simulating highly nonlinear problem analysis as like in machining is used to model the hard turning process.The model was developed using ABAQUS as large deformationsduring machiningcanbe

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efficiently modeled with integrated special features in ABAQUS.Moreover, the ABAQUS includes a wide database for the effective material modeling which allows the user to minimize the model preparation time. In this paper, temperature distribution considering the effect of varying cutting speed is also discussed using the developed numerical model of temperature distribution. Finally, important observations from the study are discussed and concluded. 1. Work piece and tool material properties In this study, AISI 52100 steelcommonly known as bearing steel was used as a work piecematerial. The hardness of work piece material is 60-62 HRC[21].The chemical composition and the material properties of the AISI 52100steel are shown in Table 1 and 2 respectively.The material properties of PCBN cutting tool is shown in Table 3. Table 1. Chemical composition and physical Properties of Bearing Steel (AISI 52100) [20]. Element

C

Si

Mn

Cu

Cr

S

Mo

Ni

P

Fe

%

0.98

0.28

0.39

0.042

1.302

0.024

0.081

0.141

0.023

Rest

Table 2. Material properties of AISI 52100 steel (HRC 62) [20] Material properties

AISI 52100

Density(Kg/m3)

7827

Inelastic heat fraction

0.9

Conductivity(W/m K)

43

Specific heat(J/Kg K)

458

Table 3. Mechanical and physical properties of the PCBN cutting tool [21]. Material properties

Carbide cutting tool

Density (Kg/m^3)

12800

Young's modulus(GPa)

800

Poisson's Ratio

0.22

Conductivity (W/m K)

82

Specific heat (J/Kg K)

226

Thermal

Expansion(m/moC)

4.9x10-6

2. Modeling procedure The turning process was modeled using a 2-D model in ABAQUS/Explicit (version 10.6). As the depth of cut was much larger than the feed rate, this model was assumed in a plane strain. It was assumed that the work piece was a deformable body and the tool was a rigid body. Work piece was modeled as a rectangle with length of 50mm and Height of 30mm. Rake angle was taken as -6⁰.The simulations for temperature distributions were performed at three different cutting speeds of 140, 200 and 260 m/min. 2.1. Material model The work piece material was modeled as plastic with isotropic hardening and the flow stress defined as function of strain, strain rate and temperature based on Johnson-Cook (J-C) constitutive model.The constants for J-C constitutive model for AISI 52100 steel are given in Table 4. Similarly, the temperature distribution properties like thermal expansion, Young’s modulus and Poisson’s ratio are given in Table 5 and 6 respectively.

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Table 4. Constants for J-C constitutive model and J-C failure model for AISI 52100 steel [22]. J-C Constants

A

B

C

n

m

Values

2482.24 MPa

1498.5 MPa

0.027

0.19

0.66

Table 5. Temperature Dependent properties for AISI 52100[21]. Temperature(⁰C)

25

204

398

704

804

Thermal Expansion(m/m⁰C)

11.5x10-6

12.6x10-6

13.7x10-6

14.9x10-6

15.3x10-6

Table 6. Temperature Dependent for AISI 52100[21]. Temperature(⁰C)

22

200

400

600

800

1000

Young's Modulus (GPa)

201.3

178.5

162.7

103.2

86.87

66.88

Poisson's Ratio

0.277

0.269

0.255

0.342

0.398

0.490

2.2. Boundary conditions In this paragraph the boundary conditions imposed in the FE model are described. Fig.2 shows a schematic diagram of the boundary conditions. The work piece was constrained along the bottom surface in the X direction and left surface in Y direction. The cutting speed was assigned at the reference point of tool. The flow chart which shows the step by step procedure to model the temperature distribution is shown in Fig. 3.

Fig. 2. Schematic representation of the boundary condition.

3. Results and discussion The temperature distribution is illustrated through simulated figures at different Cutting speeds140, 200 and 260 m/min. As the tool cuts the work piece, the cutting temperature increases and reaches maximum at a particular location on the cutting tool. The heat is generated due to severe plastic deformation in a very narrow zone (PSDZ), friction offered by the cutting tool to flowing chip and shearing of the flowing chip at the chip-tool interface(SSDZ). Fig. 4(a) illustrates temperature distribution in the work piece at cutting speed 120 m/min. It can be seen that the maximum temperature attained was 788.9ͼC and a continuous chip was produced. With a higher cutting speed of 200 m/min,the maximum temperature attained was832.92ͼC and a continuous chip was produced which can be seen from Fig. 4(b). Fig. 4(c) illustrates temperature distribution in the work piece at a cutting speed of 260 m/min. It can be seen that the maximum temperature attained was 945.88ͼC and a discontinuous type of chip was produced. From the simulated results, it can be seen that cutting temperature increases with the increase in cutting speed.The simulated results can be attributed to the fact that at lower cutting speed process employs lesser friction between the

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tool and the work piece resulting in lower heat generation and hence, the cutting temperature. The simulated results of cutting temperature at different cutting speeds are shown in Table 7.

Fig. 3. Flow chart showing step by step procedure to model temperature distribution.

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Table 7. Maximum temperature at respective cutting speeds. Cutting Speed (m/min)

Temperature (oC)

140

788.9

200

832.92

260

945.88

As chip is generally disposed in the end, it is favoured that the chip carries as much heat as possible so that tool retains small amount of heat.From the Figs. 4(a)-(c), it can be seen that the maximum amount of heat is carried away with the flowing chips as shown by red color segments in the chip. However, at higher cutting speeds the temperature at the PSDZ and SSDZ is higher in comparison to temperature obtained at lower cutting speeds. These simulated results confirm that large portion of heat is penetrated into the tool at higher cutting speedsas compared to lower cutting speeds or large portion of heat is carried away with the flowing chip at lower cutting speed in comparison to higher cutting speeds which can be seen from Figs. 4(a)-(c).

(a)

(b)

(c) Fig. 4.Temperature distribution at cutting speedsof (a) 140 m/min; (b) 200 m/min; (c) 260 m/min.

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P.S. Bapat et al. / Materials Today: Proceedings 2 (2015) 1907 – 1914 Table 8. Comparison with Experimental observation. Cutting Speed (m/min)

Experimental value of temperature (ͼC) [16]

Simulated value of temperature (ͼC)

% Error

100

605.79

798.26

24.11

175

720.64

794.16

9.25

250

872.857

831.305

-4.99

Further, the developed numerical model to obtain temperature distribution was validated with the experimental results. The simulationswere carried out at different cutting conditions reported by Shihabet al. [20]. The results obtained from simulations in the present study arecompared with the experimental observationsin[20]. The cutting conditions along with the experimental and simulated results are shown in Table 8. Percentage error in experimental and simulated results is also shown in the same Table 8. It can be seen that simulated results are not in complete agreement with the experimental results. The variations to the extent of an average error of 13% can be seen. Observed values of errors in experimental and simulated results can be attributed to the variations in the work piece and tool material properties, tool geometry and boundary conditions considered in the present study while simulating the temperature distribution. However, experimental values shown in [20] may also have some variations as it was measured by a thermocouple. Thermocouple measures temperature at a location wherever it is installed. In the present study, reported results in [20] are compared with the maximum temperature obtained by simulation. Therefore, some variations in the experimental and simulated results are expected to occur. However, average error obtained in the range of 13% shows that the developed numerical model could be used to get an idea of temperature distribution and to locate the point of maximum temperature during hard turning of AISI 52100 steel. 4. Conclusion In this study, a numerical model was developed to obtain temperature distribution in hard turning of AISI 52100 steel using PCBN tool. Temperature distribution model as a function of heat generation was developed using ABAQUS explicit and with an Arbitrary Lagrangian-Eulerian (ALE) formulation approach. A series of thermal simulations were carried out to obtain the value and region of maximum temperature at various cutting conditions. The simulatedresults of the temperature distribution showed a good agreement with the results available in the literature which showed that it is possible to carry out the complex FE model of cutting process using general purpose advanced commercial code.Based on the simulation results, at cutting speed of 260 m/min higher temperature is obtained. This is because as the cutting speed increases friction between tool and work piece also increases. The model developed could be used to predict the temperature distribution and to choose correct process parameters duringhard turning of AISI 52100 steel. References [1] C. Shet, X. Deng, Finite element analysis of the orthogonal metal cutting process. Journal of Materials Processing Technology, 105 (2002)95109. [2] A. Pal, S.K. Choudhury, S. Chinchanikar, Machinability assessment through experimental investigation during hard and soft turning of hardened steel, Procedia of Material Science 6 (2014) 80 – 91. [3] S.Chinchanikar, S.K. Choudhury, Machining of hardened steel- Experimental investigations, performance modeling and cooling techniques: A review, International Journal of Machine Tools and Manufacture, 89(2015) 95–109. [4] S.Chinchanikar, S.K. Choudhury, Hard turning using HiPIMS-coated carbide tools: Wear behavior under dry and minimum quantity lubrication (MQL),Measurement, 55 (2014) 536- 548. [5] A.P.Kulkarni, G.G.Joshi, A. Karekar, V.G. Sargade, Investigation on cutting temperature and cutting force in turning AISI 304 austenitic stainless steel using AlTiCrN coated carbide insert, International Journal of Machining and Machinability of Materials, 15 (3/4) (2014) 147156. [6] B.T. Chao, K.J. Trigger, An analytical evaluation of metal cutting temperature, Trans. ASME 73 (1951) 57–68. [7] A.P. Kulkarni, G.G.Joshi, A. Karekar, V.G. Sargade, Analytical and experimental investigation on cutting temperature in turni ng AISI 304 Austenitic stainless steel using AlTiCrN coating carbide insert, International Review of Mechanical Engineering, 7(1) (2013) 189 – 197. [8] W. C. Leone, Distribution of shear-zone heat in metal cutting, Trans. ASME 76 (1954) 121– 125.

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