Explicit To

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5728-5734 © Research India Publications. http://www.ripublication.com

Finite Element Model Based On Abaqus / Explicit To Analyze The Temperature Effects Of Turning Mr. B. V. R. M. Kumar Associate Professor, Sree Venkateswara College of Engineering, N. Rajupalem, SPSR Nellore Dt., Andhra Pradesh, India. Dr. K. Hemachandra Reddy Professor, JNTUA College of Engineering, Anantapuramu, Andhra Pradesh, India. Dr. Ch. R. Vikram Kumar Professor, N. B. K. R. Institute of Science & Technology, Vidyanagar, SPSR Nellore Dt., Andhra Pradesh, India.

process requires 3 relative motions between the tool and the workpiece. These motions are-cutting motion, forward motion and depth of motion. This process happens by continuous withdrawal of chip of a part (or) body of the test piece. The chip is usually removed by a cutting tool which has the hardness that is greater than the hardness of the body of the test piece. During the process of metal cutting, temperature is generated by the plastic deformation of the chip in the shear plane and the friction at the interface of the tool (or) chip and tool (or) body of the test piece. This generation and distribution of temperature in the cutting zone is influenced by a variety of parameters. Especially, heat dissipation depends on machining parameters, thermo-physical properties of the tool and the material and the body of the workpiece. These include thermal conductivity, thermal diffusion and heat transfer coefficient. The temperature generated during the machining process plays a very important role in determining the wear and tear of the tool, its life and the integrity of the body surface of the workpiece. It also influences the chip formation mechanism and thermal deformation of the cutting tool. The cost of machining is influenced by the material removal rate and can be brought down by increasing the cutting speed [1]. This process of increasing the cutting speed has limits in regard to the life of the tool, owing to the generation of heat in the cutting edges. An investigation of the cutting temperature is very much essential to understand the mechanism of wear of the tool material. This is very much essential for improving the efficiency of the cutting process [3]. The generation and propagation of heat is a very complex process during the turning operation as with the increase in temperature, there is a substantial change in the physical and mechanical characteristics of the metal work [3]. Also, the temperature acting on the cutting edge has a direct influence on the tool wear limiting the speed of cutting and subsequently the productivity of the machining process. There have been a variety of methods reported in the literature to study the heat generated and transmitted during the machining process and finite element methods being the latest of them all [4]. The finite element method is one of the most widely used methods to obtain the values of stress, strain,

Abstract Manufacturing industry forms the backbone for the development of a nation. To tackle the increased competition and to provide the quality service to the user, it is important to maintain the efficiency of the machining process. Turning, being a very important component of machining, it is imperative to study and analyse the process. Especially, the temperature aspect of turning is a very critical parameter having a tremendous influence on the machining process. In this work, we have developed the finite element model using ABAQUS/Explicit to analyse the temperature effects of turning. A WC tool coated with TiN is considered as tool and AISI 4340 Steel is considered to be the workpiece. JohnsonCook formulation is employed to model the workpiece and simulations were conducted to examine the temperature distribution, deformation and cutting forces. The results amply demonstrate the influence of machining factors in the process of heat generation and distribution during turning. Keywords: Turning, Finite ABAQUS/Explicit, TiN, AISI 4340.

Element

Modeling,

Introduction Current market demand forced by consumer choices has necessitated the assimilation of new technologies for sustainable development of manufacturing processes. As a consequence of this, affordable products offering a very high quality are being made available to the customers. The machining operation can be considered as one of the most common and versatile manufacturing process of the world [1]. Virtually, all mechanical assembly line is subjected to some or other type of machining operation during any of their manufacturing stages. Machining process plays a very critical and crucial role in shaping the final product [1]. Of all the machining processes, turning can be considered the simplest and one of the most widely used in industry. Turning offers many advantages like simplicity of operation, flexibility, compatibility with the environment and also very low set-up time [2]. Since the turning movement enables the part to rotate on its own axis, it allows cylindrical parts to be manufactured by means of a uniform rotary motion. This

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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5728-5734 © Research India Publications. http://www.ripublication.com deflection of complex structures”. Advances in computation and processing power have made Finite Element Analysis one of the most efficient and accurate method for modeling and analyzing different field variables. The applications for FEA include, stress analysis, analysis of heat flux, magnetic flux etc…The below section describes how the approach of Finite Element Analysis is used in analyzing the temperature distribution in machining process, through review of literature. Most of the approaches employed for numerical modeling to describe and analyze metal cutting processes falls under Lagrangian and Euclerian techniques. There is also one combinatorial technique by the name arbitrary LagrangianEuclerian formulation [12, 13]. In these techniques the mesh is attached to the work piece and the Lagrangian formulation is used. Johnson and Cooks employed constitutive equation to perform FEA with three different set of material constants. They employed FE model to analyze the behavior of Ti6Al4V alloy used in the machining process for both conventional as well as high speed operations [14]. The need to have higher productivity and demand for good quality products has dawned upon many researchers to pursue and analyze the machining parameters [15]. Different types of experimental and analytical procedures have been carried out to determine the amount of heat generated and dissipated during the machining process. Analytical and numerical procedures have also been employed to calculate the average and peak temperature at shear zone and where the primary, secondary and tertiary deformation takes place. These methods were applied for both stationary as well moving heat sources. Similarly the conduction effects of heat were also analyzed and the unknown boundary of heat flux was obtained using the interior heat distribution [16]. A numerical solution which considers a three dimensional heat diffusion equation for both the tool and tool holder assembly was proposed by S. R. Carvalho, S. M. M. Lima e Silva, A. R. Machadoa, and G. Guimaraes [17]. They employed Finite Element Method (FEM) to model the heat flow. A successful implementation of FEM was employed by. Asmaa A. kawi [18] to estimate cutting temperatures. Mofid Mahdi, Liangchi Zhang [19] employed FEM to develop a 2D cutting force model by considering chip breaking. They carefully analyzed the variation of cutting force against anisotropic materials and cutting parameters. H. S. Qi, B. Mills [20], employed the concept of the cutting interface and developed a new flow zone model during turning process. The proposed model was dynamic model and explained in detail the dynamic contact behavior between chip and the tool. Tugrul Ozel, Taylan Altan [21], adopted FEM and delivered a new approach to determine concurrently (a) the flow stress at high deformation rates and temperatures that are encountered in the cutting zone, and (b) the friction at the chip-tool interface. Xiaoping Yang, C. Richard Liu [22] designed a novel stress-based model to describe the frictional behavior during the machining process. They demonstrated the feasibility of the model using FEA. They also performed a sensitive study and compared the different results delivered by the model. Finite element based simulation have also been successfully applied for modeling metal cutting simulations based on Langrangian techniques [16]. Pradip Mujumdar [23]

displacement, shear stress, temperature distribution in the cutting zone, etc... FEM based methods results in saving of time and material when compared to the actual experimentation procedures and can provide the accurate estimation of the temperature of the cutting tool, the chip and the workpiece during the process of turning. The objective of this paper is to evolve a method based on finite element method to estimate the distribution of temperature during the process of chip formation. In this work, we have employed a finite element modelling software ABAQUS for formulating and solving the problem of temperature generation and distribution during chip formation. In addition to the temperature analysis, stress analysis has also been carried out to have dynamic comparison between the forces to establish the impact of temperature in the cutting area.

Finite Element Analysis and Turning The problem of heat during machining was introduced by F. W. Taylor in his article “On the art of Cutting Metals” published during the year 1907 [1, 5]. The primary source of heat during the chip formation process can be attributed to the plastic deformation of the workpiece in the primary shear zone. Other sources of heat include the chip friction with the tool surface in the secondary shear zone and the additional friction caused by the tool surface [6, 7, 8, and 9]. The temperature in the cutting zone is also influenced by the contact between the tool and the chip, the cutting forces and condition of friction between tool/workpiece material. A very high degree of heat can lead to softening of the material and can increase the tool wear which can result in the failure of the tool [10]. Numerous studies have reported that as the cutting speed increases in the modern day machining process, the significance of thermal aspect of heat becomes much more important. Apart from the effect the heat has on the tool life, it also greatly influences the nature of the finish and to some extent the structural integrity of the workpiece. Precise estimation of the rate at which the heat and the temperature were generated during the cutting process is very essential. Thermal energy is one of the main contributory factors that can define the accuracy of machining operations. It has been observed that more than 50% of the total errors of workpiece can be attributed to the thermal effects [11]. Complex shapes of geometric objects and their physical properties can be constructed with the help of mathematical modeling through computers. They are referred to as Finite Element Method (FEM) and the analysis through these methods as Finite Element Analysis (FEA). FEA is tool is versatile in that it can handle irregular shapes and can be employed for analyzing materials with heterogeneous properties. R. Courant during the year 1943 first proposed the concept of Finite Element Analysis (FEA). He employed Ritz method of numerical analysis. R. Courant also utilized minimization of variation calculus to propose solutions for vibration systems. The broader definition of numerical analysis was provided by Turner MJ et al... in 1956. These definitions were provide by them through a paper published about “stiffness and

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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5728-5734 © Research India Publications. http://www.ripublication.com proposed a finite element based computational model to analyze the temperature distribution in a metal cutting process. The model proposed in [23] was based on multi dimensional heat flow equations and they also accounted for heat loss with the help of convection film coefficients at the surface. It also incorporated models for heat generations within primary and secondary zones. They validated the approach by presenting the results for high speed machining of carbon steel for a range of cutting conditions. W. Grzesik [24] employed FEM to investigate the suitability to obtain solutions for cutting forces, specific cutting energy and adequate temperature for a range of coated material. The various thermal simulation results were compared with the measured cutting temperature. Different researchers have developed different methods for accurately predicting the effects of machining process.

Figure (1): Geometries of the tool and the workpiece

Finite Element Modeling Finite element method is one of the most powerful tool that allows the estimation of process variables when it is very difficult to obtain data through experimental methods. A number of programs are available to solve the problems that are coupled between efforts and temperature. Of all these approaches, the explicit method is more apt for solving simulations that have non-linear deformations. Especially, for the case of chip formation, there is a change in strain rate and temperature when the contacts happen between the tool and the workpiece. There are 3 distinct steps that are involved in any finite element analysis. They are-the pre-processing step, the simulation step and the post-processing step. In this work, we have employed the software ABAQUS/EXPLICIT. Abaqus FEA [25] released in 1978 is a software suite that can be used for Finite Element Analysis (FEA) and Computer Aided Engineering (CAD). It can be used for both modeling and analysis of mechanical components and assemblies. It is also used for visualizing the finite element analysis result. It has an independent Abaqus / Viewer which can be used for analyzing the post processing data. It finds application in automotive, aerospace and industrial production sector. Abaqus also finds wide application in academic and research institutions and provides a good collection of multiphysics capabilities, such as coupled acoustic-structural, piezoelectric, and structural-pore capabilities. It is one of the most attractive tool for production-level simulations and is typically suitable where multiple fields need to be coupled.

Figure (2): Workpiece and tool-Initial mesh

The Johnson-Cook hardening is one particular class of isotropic hardening, when the yield stress (σ0) is assumed to be of the form 0 pl n m (1) Where is an equivalent plastic strain, A, B, n and m are material properties which are usually measured at (or) below the transition temperature Troom and ˆ is a non-dimensional temperature which is defined as

1 ˆ

A B

ˆ

0

for T

Troom

( T Troom ) /(Tm elt Troom )

for Troom

T

1

for T

(2)

Tm elt

Tm elt

Where T is the current temperature and Tmelt is the melting temperature and Troom is the transition temperature defined at which there is no temperature dependency on the expression of yield stress. The material parameters must be measured at (or) below the transition temperature. With the Johnson-Cook plastic dependency to the strain rate, the yield stress can be defined as :

4. FEM Model The workpiece used in this research work is AISI 4340 Steel. To model the work piece, we have used Johnson-Cook Plastic Model as a function of strain rate and temperature for arriving at the material constitutive model. The model employed is illustrated using figure (1) and figure (2)

A

B

pl n

1 C ln

 pl 0

1

ˆm

(3)

Where and C are material properties. ABAQUS / Explicit provides a dynamic failure model very specifically modeled for Johnson-Cook plasticity model which is suitable for high strain rate deformation of metals.

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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5728-5734 © Research India Publications. http://www.ripublication.com The model is usually referred to Johnson-Cook dynamic failure model. The Johnson-Cook dynamic failure model is based on the value of equivalent plastic strain at element integration points. Failure is assumed to occur when the damage parameter exceeds 1. The damage parameter WC is defined as

Table 2: Model setup details S. No. 1. 2.

pl

w pl

Where pl f

3.

pl f

(4) 4.

is an increment of the equivalent plastic strain,

Feed Revo Depth of Work Subs Coverage(or) Rate lution cut piece tratum Coating 100 0. 06 0. 3 AISI WC TiN 4340 100 0. 15 0. 3 AISI WC TiN 4340 150 0. 06 0. 3 AISI WC TiN 4340 150 0. 15 0. 3 AISI WC TiN 4340

is the strain at failure, and the summation is performed pl f ,

The tool geometry is depicted in the table 3

is assumed to be dependent on a nondimensional plastic strain

Table 3: Tool geometry

over all increments in the analysis. The strain at failure,

 rate,

pl

0

Parameters Rake Angle Clearance Angle Approach Angle Tool tip radius (in mm. )

; a dimensionless pressure-deviatoric stress ratio,

p (where p is the pressure stress and q is the Mises stress); q and the nondimensional temperature, ˆ , defined earlier in the Johnson-Cook hardening model The dependencies are assumed to be separable and are of the form.  pl p pl (5) d 1 d 2 exp d 3 1 d 4 ln 1 d5 ˆ f 0 q

Physical properties of the different materials modeled are illustrated using the table 4. Table 4: Physical properties of different elements used in the model

Where d1-d5 are failure parameters measured at transition temperature and is the reference strain rate. The failure parameters for AISI 4340 steel are listed in the table below:

S. No. 1. 2. 3. 4.

Table 1: Failure Parameters for AISI 4340 steel used in the model S. No. 1. 2. 3. 4. 5. 6. 7. 8.

Parameter d1 d2 d3 d4 d5 Tmelt (0C) Troom (0C)

Values -60 60 950 0. 8

Value 1 -0. 8 2. 1 -0. 5 0. 0002 0. 61 1450 20

Parameters Density (kg / m3) Young’s Modulus (GPa) Poisson Coefficient Thermal Conductivity (W / m0C)

WC AISI 4340 14950 7850 400 205 0. 21 0. 30 20 44. 5

TiN 5220 600 0. 25 19. 0

In this work, the tool is modelled as an elastic material with isotropic conductvity, density and Poisson’s ratio. Similarly, the physical properties of the workpiece play a key role in proper simulation of chip formation. The chip being free to flow or break according to its physical properties, is very highly influenced by stresses, deformations and temperature. The interactions of various media involved in the chip formation process is a very complex tribological phenomena. The friction experienced between th tool and the workpiece plays a major role in determining the surface finish. For this study, the friction is modelled using Coulomb’s Friction Law, which is described by the equation :

When the failure criteria are met, the deviatroic stress components are set to zero and remain for zero for rest of the analysis. The default, the elements that meet the failure criteria are deleted. The Johnson-Cook dynamic failure model is suitable for high strain rate deformation of metals and is, therefore, highly suitable for applications to truly dynamic situations. The parameters that are used for modeling the setup are illustrated in the table 2.

Stress (τ) = µ x σ, where µ is the friction coefficient. The coulomb friction law is applicable for those cases where the normal stress is small when compared to the shear stress of the material involved. It is also important to note that the

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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5728-5734 © Research India Publications. http://www.ripublication.com friction coefficient will vary with temperature. The value of µ used in this approach is 0. 65 [24]. Meshing is an important process for simulation by Finite Element as it directly influences the accuracy of the results. In this research work, we have employed an adaptive mesh which uses Eulerian formulation for some nodes and mixed Lagragian formulation for other nodes. It is also referred to as “Adaptive Lagrangian Eulerian Method”.

friction encountered at the tool-chip interface has a contributory effect towards heat generation.

Results & Discussions The results and discussions for the proposed tool temperature analysis approach is discussed here. As indicated in Table 1, the analysis has been performed for different feed rates and by altering the revolution while maintaining a constant depth of cut. It can be inferred from the table that two different feed rates like 100 m/min. and 150 m/min. have been employed. For these feed rates, the revolution was altered between 0. 06 mm/rev. and 0. 15 mm/rev. The depth of cut was maintained at 0. 3 mm. The analysis was done for 4 different cases as illustrated in Table 2. The results of the analysis indicating the maximum temperature for each cutting condition is enlisted in a Table 5. The temperature field observed and distortion of elements during excessive temperature is illustrated in the figure 3 and figure 4.

Figure (4): High temperature field and resultant distortion of elements

It can also be inferred from Table 5 that the advancement of a tool in regard to its movement per revolution has a very significant effect on the generation and distribution of temperature during the machining process. The Table 5 also indicates that revolutions of the workpiece has a considerable impact on the maximum heat generated. At a constant feed rate of 100 m. /min., it can be observed that when the revolution increases by 0. 09 mm. /rev., the maximum temperature increases by 760C which is 9. 54% higher than the maximum temperature obtained for a revolution of 0. 06 mm. /rev. Similarly, when the feed rate is pegged at 150 m. /min. and the revolution is fixed at 0. 15 mm. /rev., it can be observed that maximum temperature increases by 8. 78%. This increase is for an increase in the revolution by 0. 09 mm. /rev.. Similarly, when the comparison is made for different feed rates while keeping the revolutions constant, it can be observed that for a revolution of 0. 06 mm. /rev. and when the feed rate is increased from 100 m. /min. to 150 m. /min., the maximum temperature increases by 460C. This amounts to a percentage rise of 5. 78% when compared with the maximum temperature for the feed rate of 100 m. /min. Similarly, for a revolution of 0. 15 mm. /rev. when the feed rate is increased from 100 m. /min. to 150 m. /min., the maximum temperature increases by 440C. This is a 5. 05% increase in temperature when compared with the feed rate of 100 m. /min.. These discussions amply demonstrate the significance of feed rate and the progress of machining represented through the mm. /rev.. In regard to the increase of temperature between the revolution rates of 0. 06 mm. /rev. and 0. 15 mm. /rev., an increase in the primary area of the shearing zone and the secondary zone necessitates an increased cutting force. Subsequently, there is a need for increased energy to shear the material which can give rise to an increase in the temperature. With regard to the forces in the cutting zone, it can be observed that there is a marked decrease in the cutting force when there is increased heat generation. This subsequently results in degradation of the mechanical properties of the work piece enabling the process of chip formation. The following table 6 illustrates the cutting forces experienced during the machining process:

Table 5: Maximum temperature observed for different cutting conditions S. Case Feed Revolutions Depth of Maximum No. Rate (vc) (f) Cut (b) Temperature (m. /min. ) mm. /rev. (mm. ) ( 0C ) 1. Case 1 100 0. 06 0. 3 796 2. Case 2 100 0. 15 0. 3 872 3. Case 3 150 0. 06 0. 3 842 4. Case 4 150 0. 15 0. 3 916

Figure (3): Distribution of temperature field during turning

It can be inferred from Table 5 that, as predicted, the machining parameters like feed rate, revolutions, etc. have a very high degree of influence on temperature generation. It can be observed from the simulations that the predominance of temperature is clearly visible in the interface between the tool/chip than in the primary shear zone region. This can be attributed to the fact that additional heat is generated duiring the sliding of the chip and also the plastic work and the

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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 8 (2016) pp 5728-5734 © Research India Publications. http://www.ripublication.com Table 6: Cutting Force as a function of time for different cutting conditions S. No. Case 1. 2. 3. 4.

Case 1 Case 2 Case 3 Case 4

Cutting Force (Fx) (N) 49 94 46 91

observed from the results that avery high degree of temperature is encountered at the tool-chip interface. Similarly, it can also be concluded that the cutting speeds also play a very critical role in the process of heat generation. The mm. /rev. indicating the progress of the workpiece also has a very significant role in the process of temperature generation. The proposed model also estimated the forces experienced by the cutting tool and the influence exerted by the machining parameters especially the feed rate on the forces. The proposed model also illustrated the deformation experienced by the workpiece which is a major contributor towards heat generation.

Cutting Force (Fy) (N) 37 66 34 63

It can be observed that one of the primary sources of heat generation and distribution in the chip formation process is the plastic deformation in the primary shear zone. It can also be observed that the maximum deformation occurs when the feed rate is maximum and the revolution per mm. is maximum. The process of plastic deformation happens very close to the cutting edge. With a very high feed rate and mm. /rev., the machining conditions with the resultant heat generated can influence the dimensional accuracy of the machining. The stress and strain experienced by the workpeice is depicted using figure 5 and figure 6.

References [1]

[2] [3]

[4]

[5]

[6] Figure (5): Von-Mises equivalent stress plot according to processing

[7]

[8]

[9]

[10]

[11]

Figure (6): Equivalent plastic strain plot according to processing

[12] Conclusion The proposed model has the capability to serve as an effective alternative for experimental studies. The model is capable of estimating the distribution of the temperature, stress and strain that is encountered during the turning process. It can be

[13]

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Trent, E. M., 1991. Metal Cutting. 3rd Edn., Buterworth-Heinemenn, Oxford, UK., ISBN-13: 9780750610681, Pages: 273. Machado, R. and R. Alisson, 1988. The evolution of materials for cutting tools. Mach. Metals, 268: 92-98. Ren, X. J., Q. X. Yang, R. D. James and L. Wang. Cutting temperatures in hard turning chromium hardfacings with PCBN tooling. J. Mater. Process. Technol., 147: 38-44., 2004 Mahnama, M. and M. R. Movahhedy, Application of FEM simulation of chip formation to stability analysis in orthogonal cutting process. J. Manuf. Process., 14: 188-194, 2012. Longbottom, J. M. and J. D. Lanham. A review of research related to Salomon's hypothesis on cutting speeds and temperatures. Int. J. Machine Tools Manufac., 46: 1740-1747, 2006. Rech, J., J. L. Battaglia and A. Moisan, Thermal influence of cutting tool coatings. J. Mater. Process. Technol., 159: 119-124, 2005 Grzesik, W. and P. Nieslony, A computational approach to evaluate temperature and heat partition in machining with multilayer coated tools. Int. J. Mach. Tools Manuf., 43: 1311-1317, 2003. Singamneni, S. B, A mixed solution for the threedimensional temperature distribution in turning inserts using finite and boundary element techniques. J. Mater. Process. Technol., 166: 98-106,, 2005 O’Sullivan, D. and M. Cotterell, Temperature measurement in single point turning. J. Master. Process. Technol., 118: 301-308,, 2001. Zhou, J. M., M. Andersson and J. E. Stahl, 2004. Identification of cutting errors in precision hard turning process. J. Mater. Process. Technol., 153154: 746-750. M. J. Turner, R. W. Clough, H. C. MactinandL. J. Topp. Stiffness and defection analysis of complex structure. J. aeronaut science1956; 23:805-823. M. H. El-Axir. A methode of modeling residual stress distribution in turning for different material., International journal of Machine tool & Manufacture 42 (2002) 1055-1063 O. Pantal_e, J.-L. Bacaria, O. Dalverny, R. Rakotomalala, S. Caperaa. 2D and 3D numerical

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