EXPERIMENTAL VALIDATION OF TURNING PROCESS USING 3D FINITE ELEMENT SIMULATIONS

CHAPTER-5 EXPERIMENTAL VALIDATION OF TURNING PROCESS USING 3D FINITE ELEMENT SIMULATIONS This chapter presents the three-dimensional (3D) finite element analysis (FEA) to calculate the workpiece tool wear and displacement amplitude in face turning operation of AISI 4140 and AISI 1040 steel work pieces under dry machining condition using both coated and uncoated carbide inserts. 3D turning models have been developed and validated by comparing with experimental results of vibration induced turning. Present chapter objective is to develop a methodology to predict the displacement amplitude, load in feed direction, tool wear evolution and chip formation in turning process using FEM simulations. Test conditions mentioned in table 3.3 of chapter 3 are used in the development of FE modeling and simulation of turning process. These FE simulations are validated with results of extensive experimentation and investigations carried out on lathe machine in chapter 4. 5.1. INTRODUCTION Specially designed commercial FE programme i.e., DEFORM 3Dv 6.1 software is used for simulating cutting operations because of its designing and analysis tools. The effect of workpiece displacement amplitude due to vibration on the tool wear is examined. The predicted load and corresponding displacements from the FE simulation strongly correlated with the experimental results. The conclusion states that the initiated models can be for industrial application use. Metal cutting researchers focus on determining the best cutting conditions and tool geometries for improved process efficiency. Experimental works are needed to obtain results but they are expensive and time consuming. At this point numerical methods become important. In last two decades, Metal cutting analysis is done by finite element method (FEM). Different outcomes and characterized of the machining processes such as cutting forces, stresses, temperatures, chip shape, etc. can be predicted by using FEM without doing any experiment. In this chapter, some basic aspects of finite element simulation of metal cutting are presented. In the present work investigation primarily focuses on preparing a 3D finite element simulation based methodology to forecast the development of vibration 79

displacement, load prediction and tool wear in face turning. Specifically, the research tasks in this chapter include: to provide an appropriate experimental data to make a correlation model for tool wear so as to look into the influence of cutting tool vibrations under the various cutting conditions. Execution of metal cutting model(s) in the commercial FEM code (Deform -3Dv6.1) that transmits the displacement and tool wear during machining to the predicted process variables. Results show the good degree of agreement between experimental and simulated values in predicting worn tool profiles at different cutting conditions. 5.2 METHODOLOGY FOR RESULTS VALIDATION WITH FE SIMULATIONS In the present work, tool chip and tool workpiece interfaces is strongly influenced by the displacement amplitude due to vibration, tool wear, cutting temperature and relative sliding velocity at the interface. Therefore, understanding of the tool wear behavior and the capability of predicting it is the key to successful process control and optimization [148]. To achieve this goal, the effect of different cutting variables and process mechanics has second part includes the validation FE simulation results with experimental results considered in the literature. Overview of proposed methodology in present study is presented in figure 5.1 is adopted to investigate experimental results using finite element simulations. Hence ultimate objective of the present study is to develop a 3D finite element simulation based methodology to predict the evolution of displacement amplitude and tool wear in vibration induced face turning. Implementation of machining model(s) in the commercial FEM code (DEFORM -3Dv6.1) that relates the displacement amplitude and tool wear during machining to the predicted process variables. This approach consists of development of tool wear model for the specified tool work piece pair via a calibration set of tool wear cutting tests in conjunction with FE cutting simulations. Turning operation is a steady-state process when continuous chip is formed. The implementation of tool wear estimation is relatively easier and studied first. A tool wear estimation models have been developed for turning operation as part of the work. Present study is planned to carry out in following stages, first stage concentrates on chip formation analysis in which a new chip formation modeling method for continuous steady state chip formation is developed. It can simulate the entire chip formation process 80

from initial chip formation, chip growth to steady state by making use of Arbitrary Lagrangian Eulerian technique. Figure 5.1 Proposed methodology for validating the experimental results with FE simulation Second stage concentrates on tool chip interface temperature analysis. In order to save the calculation time and the temperature distribution in the cutting tool at thermal steady state is studied by performing pure heat transfer analysis. Tool wear estimation models for 81

turning mainly composed of chip formation analysis, heat transfer analysis, wear calculation procedure and tool geometry updating, as shown in figure 2. Figure 5.2 Overview of finite element simulations 82

Present analysis is confined to only to study the temperature distributes in the cutting tool. Last stage involves in modeling the displacement amplitude and its corresponding predicted load. Through previous stages, the present commercial FE code also predicts the tool wear along with normal stress, sliding velocity and tool temperature at steady state according to the test conditions. Thus, an extensive investigation on the effect of displacement amplitude of workpiece due to vibration on tool wear in face turning processes is important to realize an accurate finite element simulation (FES) method for the vibration induced machining (VIM). 5.3 STEPS IN FINITE ELEMENT MODELING OF TURNING PROCESSES Three main model formulations are used in finite element modeling of metal cutting: Lagrangian, Eulerian and Arbitrary Lagrangian-Eulerian (ALE). 5.3.1 Lagrangian Lagrangian formulation is mainly used in solid mechanics problems. Here the FE mesh is attached to workpiece material and cover the whole of the region under analysis. This makes it highly preferable when unconstrained flow of material is involved. Lagrangian formulation is broadly used in metal cutting simulation due to ability to determine geometry of the chip from incipient stage to steady state and this geometry is a function of cutting parameters, plastic deformation process and material properties. As a result, boundaries and shape of the chip do not have to be known a priori. Besides, chip separation criteria can be defined to simulate discontinuous chips or material fracture in metal cutting models that are based on Lagrangian formulation. Although there are many advantages of Lagrangian formulation, it has also shortcomings. Metal being cut is exposed severe plastic deformation and it causes distortion of the elements. Therefore, mesh regeneration is needed. Secondly, chip separation criteria must be provided. This drawback of formulation can be eliminated by using an updated Lagrangian formulation with mesh adapativity or automatic remeshing technique. 5.3.2 Eulerian In Eulerian formulation, the FE mesh is spatially fixed and the material flow through the control volume that eliminates element distortion during process. Besides, fewer elements required for the analysis, thereby reducing the computation time. Cutting 83

is simulated from the steady state and therefore there is no need for separation criteria in Eulerian based models. The drawback of Eulerian formulation is a need in determining the boundaries and the shape of the chip prior to the simulation. Also the chip thickness, the tool chip contact length and the contact conditions between tool-chip must be kept constant during analysis that makes Eulerian formulation does not correspond to the real deformation process during metal cutting. 5.3.3 Arbitrary Lagrangian-Eulerian (ALE) The best features of Lagrangian and Eulerian formulations have been combined and called arbitrary Lagrangian-Eulerian (ALE). In ALE formulation, the FE mesh is neither fixed spatially nor attached to the workpiece material. The mesh follows the material flow and problem is solved for displacements in Lagrangian step, while the mesh is repositioned and problem is solved for velocities in Eulerian step. The idea used in metal cutting simulation is to utilize Eulerian approach for modeling the area around the tool tip where cutting process occurs. As a result, severe element distortion is avoided without using remeshing. Lagrangian approach is utilized for the unconstrained flow of material at free boundaries. Furthermore shape of the chip occurs as a function of plastic deformation of the material. 5.4 CONSTITUTIVE MODELS FOR WORK PIECE AND CUTTING TOOL MATERIALS One of the major topic in machining simulation is modeling flow stress of workpiece material to generate true results. Flow stress is an instant yield stress and it depends on temperature and strain rate signified by the mathematical forms of constitutive equations. Another type of metal cutting simulations is Oxley, Johnson - Cook and Zerilli-Armstrong material constitutive models. 5.4.1. Material Flow Properties In FEA simulation of a metal cutting process, Material properties are essential inputs. To identify the constitutive behavior of the cutting tool and workpiece materials under specific cutting parameters, it is compulsory to have an precise and reliable flow stress model that is the strength of representing hardness, strain rate and thermal. Johnson-Cook Model is used to study dynamic events and it is problem solving of fast 84

deformations and large strain, e.g. machining simulations [149]. According to a comparative analysis described by Johnson- Cook model is one of the most convenient material models that also produce excellent results describing the material behavior and chip formation. In this work, the Johnson- Cook constitutive model was used to predict the post-yield behavior of AISI 1040 steel is given by equation 5.1 is used [150]. =( + E [1+ E E ][1 ] (5.1) where E is plastic strain rate, E is equivalent plastic strain, E is reference strain rate, A is initial yield stress, B is hardening modulus, C is strain rate dependency coefficient, n is work hardening exponent, m is thermal softening coefficient, θ is the process temperature, θ is the melting temperature of the workpiece and θ is the ambient temperature (35 0 C). The Johnson-Cook model provides a good fit for the mechanical behavior of metals, it is numerically robust and can easily be used in FEA models. In general, the parameters A, B, C, n and m of the model are fitted to the data obtained by several material tests conducted at low strains and strain rates and at room temperature as well as split Hopkinson pressure bar (SHPB) tests at strain rates up to 1000/s and at temperatures up to 600 C [151]. Many researchers used the Johnson-Cook model as constitutive equation in FEA modeling of orthogonal machining (see Table 5.1). Remarkable differences between values cited for the same material type can be observed. These are most probably related to specific material and test conditions. In this section, table 5.1 gives the Johnson cook parameters for both AISI 1040 and AISI 4140 steel specimens. Table 5.2 presents the chemical and physical properties of uncoated carbide tool material whereas table 5.3 gives the physical properties of tungsten carbide insert tool material [152]. Chemical and physical properties of coated carbide tool material are shown in table 5.4 and coating layer physical properties are presented [154] in table 5.5. Data presented in the tables is very much needed to modelling and simulation of machining process as per the test conditions. Table 5.1 Johnson cook parameters [151] 85

Property AISI 1040 steel AISI 4140 steel A 353.4 806 B 102.6 614 C 0.298 0.168 n 0.210 0.0089 m 7.99436 1 Table 5.2 Chemical and physical properties of uncoated carbide tool material [152] WC Co Tac Nbc Hardness (Vol. %) (Vol. %) (Vol. %) (Vol. %) (HV) 81 17.1 1.2 0.6 1500 Table 5.3 Physical properties of Tungsten carbide [152] Property Temperature( 0 C) 20 200 400 600 Modulus of elasticity (GPa) 580 570 560 540 Poisson ratio 0.22 0.22 0.22 0.22 Density (kg/m 3 ) 14,500 - - - Thermal expansion coefficient ( 10 6 ; K 1 ) 5.4 5.3 5.4 5.6 Thermal conductivity (W/m C) 30 32.5 35 38 Heat capacity (N/mm 2 C) 220 - - - Table 5.4 Chemical and physical properties of coated carbide tool material [153] WC (Vol.%) Co (Vol.%) Tac (Vol.%) Nbc (Vol.%) Hardness (HV) Grain size(µm) K1C [MPa (mk1/2)] Coating thickness (µm) TiCN Al 2O 3 TiN 81 17.1 1.2 0.6 2000 1.7 14 4 1 0.5 86

Table 5.5 Physical properties of coating layers [154] Property/Coating material TiN TiCN Al2O3 Coating thickness (µm) 1.5-3 4-8 3-5 Hardness (HV) 2300 3000 2000 Thermal expansion coefficient ( 10 6 ; K 1 ) 9.4 8 9.4 Modulus of elasticity (GPa) 600 448 415 Poisson ratio 0.25 0.23 0.22 Density (kg/m3) 4650 4180 3780 Heat capacity (N/mm2 C) 3 2.5 3.42 Thermal conductivity (W/m C) 20(40 0 C 26(25 0 C 33(50 0 C) By using the properties data presented in the above tables, both workpiece and cutting tool inserts are modeled using Deform 3D software. FE models of AISI 4140 steel and AISI 1040 steel work pieces are presented in figure 5.3. Figure 5.3(a) gives the AISI 4140 steel whereas figure 5.3(b) shows the FE model of AISI 1040 steel. Similarly, carbide insert tools also modeled using Deform 3D by taking all the properties into account and they presented in figure 5.4. Figure 5.4(a) gives the FE model of uncoated DNMA carbide insert and figure 5.4(b) shows the coated carbide insert model. Figure 5.3(a) Modeling of 4140 workpiece Figure 5.3(b) Modeling of AISI 1040 steel 87

Figure 5.4(a) Modeling of uncoated insert 5.5 MESHING AND BOUNDARY CONDITIONS 88 Figure 5.4(b) Modeling of coated insert A continuous region is divided discrete region called elements in FE analysis. This procedure is called discretization or meshing. Initial designed FE mesh cannot hold its original shape and it is distorted due to severe plastic deformation during metal cutting or metal forming processes. The distortion causes convergence rate and numerical errors. To handle with this problem a new FE mesh must be generated in means of changing the size and distribution of the mesh. This is called adaptive mesh procedure. One of adaptive mesh procedure is re-meshing technique and it includes the generation of a completely new FE mesh out of the existing distorted mesh. Second one is called refinement technique that is based on increasing the local mesh density by reducing the local element size. The last adaptive mesh technique is smoothing which includes reallocating the nodes to provide better element shapes. The adaptive mesh procedure decreases solution errors during calculation therefore it increases the accuracy of the simulation. For these reasons, the adaptive mesh procedure must be used in FE simulations including severe plastic deformation such as metal cutting and metal forming. Table 5.6 Boundary conditions [154] Property DNMA 432- Uncoated carbide insert Initial Temperature ( o C) 20 20 Shear friction factor 0.6 0.6 Heat transfer coefficient at the interface (N/s mm C) 45 45 DNMA 432- Coated carbide insert As the boundary conditions presented in table 5.6, machining system for FT-UC and FT-C are developed in the Deform 3D environment. Figure 5.5 presents the meshed

model of FT-UC machining system with boundary conditions for AISI 4140 steel and uncoated carbide insert combination and figure 5.6 gives meshed model of the FT-C machining system with boundary conditions for AISI 1040 steel and coated carbide insert combination. Figure 5.5 FT-UC machining system with boundary conditions (meshed model) Figure 5.6 FT-C machining system with boundary conditions (meshed model) 89

5.6 THREE DIMENSIONAL (3D) FINITE ELEMENT SIMULATION OF TURNING PROCESS In this section figure 5.7 gives the FT-UC and figure 5.8 presents the FT-C combination. This section presents the three dimensional models for FT-UC and FT-C machining processes. Figure 5.7 FE modeling of displacement amplitude due to vibration in FT-UC turning Figure 5.8 FE modeling of displacement amplitude due to vibration in FT-C turning 90

Implemented 3D models in DEFORM 3D v6.1, are reported in figure 5.7 and figure 5.8. Where it is possible to see the workpiece with the growing chip and the tool. The tool, a rigid object meshed with more than 1, 00,000 elements, is oriented according to the cutting angles set in experimental test and reported in table 3.3 and it moves along the feed direction. Workpiece considered as an elasto- plastic object meshed with more than 30,000 elements, is fully constrained on the lower and lateral sides so it cannot move. Thermal boundary conditions are set on the same faces so that to simulate the heat diffusion. In contact between tool and chip, thermal exchange is done and it is regulated by heat diffusion relationship whose characteristic parameters are reported in table 5.6. The friction is modeled considering a shear factor equal to 0.82. An adaptive re-meshing scheme is implemented to optimize between the computational time and accurate prediction. The top and back surfaces of the tool are fixed in all directions. The workpiece is constrained in vertical (Z) and lateral (Y) directions on the bottom surface and moves at the cutting speed in the horizontal direction (X) toward the stationary tool. In this work, a commercial software code, DEFORM 3Dv6.1 and explicit dynamic ALE modeling approach is used to conduct the FEM simulation of oblique cutting process. The chip formation is simulated via adaptive meshing and plastic flow of work material. Therefore, there is no chip separation criterion is needed. In this approach, the elements are attached to the material and the unreformed tool is advanced towards the workpiece. The programme menus are designed in such a way that they allow the user to minimize the model preparation time. The thermo-mechanical FEM simulation schemes (models) are created by including tool and workpiece thermal and mechanical properties, boundary conditions, contact conditions between tool and workpiece as shown in figures 5.7 and figure 5.8 and as per the conditions given in tables 5.1 and table 5.6. As discussed in literature, this research is aimed to the 3D numerical prediction of tool wear using the knowledge acquired in 2D studies. Thus, in order to validate 3D predictions for both flank wear and displacement amplitude due to vibration, experimental details are reported in chapter 4 have been considered for the present section. Tool rejection criterion developed based on both ISO 3685 and ISO 10816 standards for evaluate the cutting tool condition in turning. 91

5.7 RESULTS AND DISCUSSION OF 3D FE SIMULATIONS In this section, finite element simulations have been carried out for both coated (with AISI1040 steel) and uncoated carbide insert (with AISI 4140 steel). For each combination, 27 cases of experiments consisting of different machining combinations are modeled and simulated using commercial FE simulation software deform 3Dv6.1. Experimental findings like displacement due to vibration, cutting force in feed direction and tool flank wear from previous chapter are evaluated with finite element predicted results. In FE simulations, the resultant field variables have been extracted after five milliseconds (ms) of cutting time. This short cutting time is assumed sufficient for the field variables to reach a steady-state. The combination of coated carbide insert with AISI 1040 steel is denoted with FT-C whereas uncoated carbide insert with AISI 4140 steel is denoted with FT-UC. Results of test conditions 1-1, 2-5 and 3-9 are discussed in detail about resultant field variables whereas Table 2 gives the details of entire experimentation. 5.7.1 Evaluation of Displacement Due To Vibration (TC1-1: Rotational Speed 538 Rpm, Feed At 0.08mm/Rev, Depth of Cut 0.5 Mm) The 3D models, implemented in Deform 3D v6.1, are reported in figure 5.9, where it is observed that workpiece with the growing chip and the tool. Figure 5.9(a) presents the FE predicted displacement when AISI 4140 steel is machined with uncoated carbide insert (FT-UC:1-1) and this value is compared with the measured displacement value and it is presented in figure 5.9(b). Similarly, Figure 5.9(c) gives the FE predicted displacement in case of AISI 1040 steel specimens machined with coated carbide insert (FT-C:1-1). Figure 5.9(d) shows the measured displacement using laser Doppler vibrometer (LDV) to compare with FE simulated results. Maximum error is found be 13% that shows the good agreement between the two results. In agreement with experimental results [155,156] the chip is a continuous one due to material model and cutting condition chosen. Displacement predicted from FEM simulation are also maintains good agreement with the results of experiment. It is a known fact that the friction between the tool and the workpiece is little when the tool is new (sharp) and accordingly the amplitude of vibration will also be low. In case FT-UC, it is observed that, there is an increased level of vibration and this is due to increase in tool wear of uncoated carbide insert. 92

Uncoated DNMA 432/AISI 4140 steel FT-UC 1-1 :( Rotational speed 538 rpm, feed at 0.08mm/rev, depth of cut 0.4 mm) Comparsion of displacement due to vibration Figure 5.9 (a) FE predicted displacement Coated DNMA 432/AISI 1040 steel Figure 5.9 (b) Measured displacement using Laser Doppler Vibrometer Figure 5.9 (c) FE predicted displacement 93 Figure 5.9 (d) Measured displacement using Laser Doppler Vibrometer Owing to its strength coated carbide insert tool exhibits good resistance to wear. Relatively lesser displacement value is recorded with laser Doppler vibrometer in the case of FT-C. No built-up edge is found in both the cases. 5.7.2 Evaluation of Cutting Force during Turning (TC 2-5 Rotational Speed 836 Rpm, Feed at 0.4 Mm/Rev, Depth of Cut 0.8 Mm) Figure 5.10(a) and Figure 5.10(c) shows a FEM simulated typical cutting force versus time increment values for test conditions FT-UC:2-5 and FT-C:2-5. Results of test

condition 2-5 are given in figure 5.10 for both FT-UC and FT-C combinations. In figure 5.10(b) measured cutting force for test condition FT-UC:2-5 is presented and figure 5.10(d) gives the cutting force value for test condition FT-C:2-5. Difference in cutting force value is clearly evident. As the machining time increases, the strength and stiffness of the uncoated cutting tool gets effected which resulted in increased cutting force value when compared with coated cutting insert at similar test condition. The same trend is observed in the all combinations of FT-UC: 2 & FT-C:2. At the end of TC 2-5 condition, built-up edge (BUE) is started to form. Uncoated DNMA 432/AISI 4140 FT-UC: 2-5 Rotational speed 836 rpm, feed at 0.4 mm/rev, depth of cut 0.8 mm) Comparsion of Load/Force during machining Figure 5.10 (a) FE predicted load in feed direction Coated DNMA 432/AISI 1040 Figure 5.10 (b) Measured cutting force using kistler dynamometer Figure 5.10 (c) FE predicted load in feed direction Figure 5.10 (d) Measured cutting force using kistler dynamometer 94

5.7.3 Evaluation Of Tool Flank Wear (TC 3-9 Rotational Speed 1135 Rpm, Feed At 0.8 Mm/Rev, Depth Of Cut 1.2 Mm) Figure 5.11(a) shows the simulated tool flank wear corresponding to uncoated carbide insert for machining AISI 4140 (FT-UC:3-9). FEM predicted tool flank wear for coated carbide insert is presented in Figure 5.11(b). Difference between the tool wear profiles is clearly observed in FE simulations as well measured profiles also. Uncoated DNMA 432/AISI 4140 FT-UC :3-9 (Rotational speed 1135 rpm, feed at 0.8 mm/rev, depth of cut 1.2 mm) Comparsion of tool flank wear during machining Figure 5.11(a) FE predicted tool flank wear Figure 5.11(b) Tool flank wear under vision inspection system Coated DNMA 432/AISI 1040 Figure 5.11(c) FE predicted load in feed direction Figure 5.11(d) Tool flank wear under vision inspection system 95

Tool flank wear profiles are measured under vision inspection system are shown in figure 5.11(c) and figure 5.11(d). Figure 5.11(c) gives the profile of uncoated carbide insert tool whereas figure 5.11(d) shows the flank wear profile of coated carbide insert. It is found that when the flank wear developed further near VB=0.2mm formation of built-up on the edge and material smearing on the flank wear face can be observed. It is observed that as long as values of VB= 0.25 mm the tool flank wear developed gradually with significant BUE formation. From the FE simulations, it is observed that as long as values of VB 0.14mm the tool flank wear developed gradually without any BUE formation. Table 5.7 gives the results of the experiment with FT-UC and table 5.8 presents the results of FT-C. From table 5.7 and table 5.8, it is found that according to ISO 10816 displacement amplitude values up to 20 µm do not have any effect on tool flank wear. Tool flank wear is found to be effected by the measured displacements in the range between 20 and 60 µm. A displacement value beyond 60 µm is not a acceptable as per ISO 10816. Table 5.7 Experimental and Simulated values (FT-UC Turning) Test condition Dispalcement µ m Flank wear,vb (mm) Expt. Predt. Expt. Predt. Expt. Cutting force in feed direction,fy (N) FE predicted Machining Time, (sec) FE simulated FT-UC 1-1 14 12.86 0.08 0.076 21.52 19.58 8.22 FT-UC 1-2 16 14.74 0.12 0.119 34.42 30.97 12.22 FT-UC 1-3 18 17.40 0.14 0.136 64.56 56.81 16.22 FT-UC 1-4 20 18.84 0.17 0.163 107.60 95.76 20.22 FT-UC 1-5 22 21.40 0.20 0.189 172.16 146.33 24.22 FT-UC 1-6 28 27.62 0.21 0.206 322.80 277.60 28.22 FT-UC 1-7 30 29.92 0.23 0.221 215.20 193.68 32.22 FT-UC 1-8 33 31.30 0.25 0.242 339.32 295.20 36.22 FT-UC 1-9 35 33.40 0.28 0.273 645.60 574.58 40.22 FT-UC 2-1 19 18.22 0.16 0.152 33.44 29.76 4.10 96

FT-UC 2-2 23 21.92 0.18 0.172 53.50 47.08 6.05 FT-UC 2-3 25 23.80 0.19 0.187 100.32 86.27 8.11 FT-UC 2-4 27 26.12 0.23 0.226 167.2 152.15 10.04 FT-UC 2-5 35 33.84 0.26 0.254 267.52 243.44 11.58 FT-UC 2-6 43 42.08 0.31 0.304 501.6 446.42 13.33 FT-UC 2-7 53 51.50 0.32 0.315 334.45 294.31 15.48 FT-UC 2-8 62 61.20 0.34 0.327 535.04 470.83 17.33 FT-UC 2-9 75 73.24 0.36 0.353 998.2 868.43 19.28 FT-UC 3-1 15 14.40 0.22 0.219 45.4 39.95 1.5 FT-UC 3-2 20 18.56 0.24 0.238 72.64 66.10 2.25 FT-UC 3-3 24 23.20 0.26 0.254 136.2 122.58 3.03 FT-UC 3-4 29 28.88 0.36 0.361 227 197.49 3.78 FT-UC 3-5 41 39.06 0.39 0.387 363.2 323.24 4.58 FT-UC 3-6 52 51.48 0.43 0.423 681 585.66 5.45 FT-UC 3-7 64 62.57 0.47 0.465 454 399.52 6.13 FT-UC 3-8 73 71.08 0.49 0.489 726.4 653.76 6.9 FT-UC 3-9 89 87.96 0.54 0.529 1247.5 1060.37 7.66 Table 5.8 Experimental and Simulated values (FT-C Turning) Test condition Dispalcement µ m Flank wear,vb (mm) Expt. Predt. Expt. Predt. Expt. Cutting force in feed direction, Fy (N) FE predicted Machining Time, (sec) FE simulated FT-C 1-1 11 09.86 0.06 0.066 20.44 18.60 6.22 FT-C 1-2 14 12.74 0.09 0.091 31.32 27.56 10.12 FT-C 1-3 16 14.80 0.11 0.106 60.68 51.58 13.32 FT-C 1-4 18 16.84 0.13 0.129 102.22 93.02 17.04 FT-C 1-5 20 18.40 0.16 0.158 163.55 145.56 20.22 97

FT-C 1-6 26 24.62 0.18 0.173 306.66 269.86 24.25 FT-C 1-7 28 26.92 0.19 0.185 197.98 172.24 29.30 FT-C 1-8 30 28.30 0.23 0.228 315.56 271.38 33.42 FT-C 1-9 32 30.40 0.25 0.249 613.32 539.72 36.51 FT-C 2-1 16 15.2 0.14 0.139 32.10 29.21 2.10 FT-C 2-2 20 18.2 0.15 0.148 50.29 42.74 4.57 FT-C 2-3 22 21.8 0.16 0.157 92.29 82.142 7.55 FT-C 2-4 24 22.12 0.20 0.195 157.16 135.16 9.54 FT-C 2-5 32 30.84 0.25 0.247 256.81 218.29 10.58 FT-C 2-6 40 38.80 0.30 0.292 466.48 419.83 11.33 FT-C 2-7 50 48.50 0.31 0.302 317.72 276.42 14.58 FT-C 2-8 60 58.20 0.32 0.318 502.93 447.61 16.33 FT-C 2-9 72 69.84 0.33 0.323 928.32 798.36 18.08 FT-C 3-1 12 11.40 0.21 0.209 41.76 37.17 1.02 FT-C 3-2 16 15.56 0.23 0.228 66.10 60.15 2.05 FT-C 3-3 22 21.40 0.26 0.254 128.02 111.38 3.13 FT-C 3-4 26 24.88 0.36 0.356 213.38 201.94 3.58 FT-C 3-5 38 36.06 0.39 0.380 345.04 293.28 4.28 FT-C 3-6 48 47.48 0.40 0.393 640.14 582.52 5.35 FT-C 3-7 60 58.27 0.43 0.423 413.14 363.56 5.59 FT-C 3-8 68 67.08 0.44 0.441 690.08 593.46 6.09 FT-C 3-9 74 73.66 0.46 0.452 1172.65 1055.38 7.56 Results in the tables 5.7 and table 5.8, clearly indicates the same in all test conditions i.e, (FT-UC & FT-C):1-1 to 1-9 displacement values is found to be less than 60 microns and corresponding VB 0.3 mm. With increase of flank wear, displacement amplitude due to vibration is found to be increased in all test conditions. As it can be observed from tables, the tool life is reduced significantly with the increasing vibration amplitude at all test 98

conditions. If the values of flank wear (VB) values are found to be more than 0.3 mm it is a clear indication of the end of the tool life as per ISO 3685 and rejection criterion adopted in present study. 5.8 SUMMARY OF 3D FINITE ELEMENT SIMULATIONS In this work, a finite element model of chip formation process in face turning is combined with a dynamic model of machine tool system in order to obtain a comprehensive model which realistically predicts the effects of various cutting parameters on displacement amplitude of the workpiece in face turning. A Lagrangian FE approach is used in combination with mesh adaptation techniques to deal with severe mesh distortion in machining process simulations. Using the combined model, the displacement amplitude of the workpiece is determined at different cutting conditions. In particular, the effects of depth of cut at constant cutting speed is studied which defines the onset of instability. Also, the effect of cutting speed on the vibration amplitude is investigated. The simulation results show a good agreement with experimental results in the range of cutting speed and feed rate considered. It is shown that the stability borders obtained at mid-to-high speeds are in agreement with the results considered in the present work for investigation. Furthermore, 3D FE models are able to predict the tool wear and corresponding load during the cutting process. It is demonstrated that the FE simulations can be used successfully to distinguish between new and worn tools. This is very meaningful for the scientific research and education. A high degree of identified between the experimental and FE simulated results in identifying the effect of vibration amplitude on tool wear state. 99