CHAPTER 7 PREDICTION OF TEMPERATURE DISTRIBUTION ON CUTTING TOOL

Transcription

1 142 CHAPTER 7 PREDICTION OF TEMPERATURE DISTRIBUTION ON CUTTING TOOL The main objective of this chapter is to predict the temperature distribution on a turning tool by using the following methods: Build a FEA model of the turning tool assembly and perform transient thermal analysis by using ANSYS software. Experimental methods ( using thermocouple) for validation 7.1 HEAT GENERATION IN THE ORTHOGONAL CUTTING When hard turning, the cutting zone temperature is approximately 926 C and the majority of the heat from the cutting should be carried away with the chip. As heat increases and tool wears (Davami et al 2008). Proper application of speed and feeds are critical. The main regions where heat generated during the orthogonal cutting process are shown in Figure 7.1. Firstly, heat is generated in the primary deformation zone due to plastic work done at the shear plane. Secondly, heat is generated in the secondary deformation zone due to work done in deforming the chip and in overcoming the sliding friction at the tool chip interface zone. Finally, the heat generated in the tertiary deformation zone, at the tool workpiece interface is due to the work done to overcome friction, which occurs at the rubbing contact between the tool flank face and the newly machined surface of the workpiece. Heat generation in this zone is a function of the tool flank wear.

2 143 Figure 7.1 Sources of heat generation in the orthogonal cutting process Heat is removed from the primary, secondary and tertiary zones by the chip, the tool and the workpiece. Figure 7.2 schematically shows this dissipation of heat. The temperature rise in the cutting tool is mainly due to the secondary heat source, but the primary heat source also contributes towards the temperature rise of the cutting tool and indirectly affects the temperature distribution on the tool rake face. Figure 7.2 Schematic representation of a heat transfer model in orthogonal metal cutting

3 144 During the process, part of the heat generated at the shear plane flows by convection into the chip and then through the interface zone into the cutting tool. Therefore, the heat generated at the shear zone affects the temperature distributions of both the tool and the chip sides of the tool chip interface, and the temperature rise on the tool rake face is due to the combined effect of the heat generated in the primary and secondary zones. 7.2 NEED FOR TEMPERATURE PREDICTION High temperature in the tool-chip interface increases the rate of wear. During machining, a tool penetrates into the workpiece because of the relative motion between the tool and the workpiece, deforms the work material plastically, and removes the material in the form of chips. Plastic deformation of the work material, rubbing of the tool flank with the finished surface and friction between tool rake face and flowing chips produce huge amount of heat and intense temperature at the chip tool interface. A major portion of the energy is consumed in the formation and removal of chips. Energy consumption increases with the increase in cutting velocity, feed and depth of cut as well as strength and hardness of work material. When the energy consumption is greater, the temperature and frictional forces becomes greater at the tool-chip interface and consequently higher is the tools wears and lower the tool life. 7.3 FINITE ELEMENT SIMULATION OF THE HEAT TRANSFER PROCESS A commercial finite element analysis system ANSYS11.0/Standard is used to solve the transient heat transfer problem and simulate the temperature distribution in the cutting tool insert. The analysis is based on the estimated machining heat flux applied over the tool-chip contact area on the rake face of the cutting insert.

4 145 During the cutting process, a significant amount of heat penetrates into the shim seat, the clamping parts and the holder, and the computational domain cannot be considered as the cutting insert only. Model is developed which consists of VP coated carbide insert, shim seat and the tool holder as shown in Figure 7.3 and 7.4. Small details of the clamping parts are neglected in the model due to certain limitations of the analysis package. Figure 7.3 Pro-E model of tool holder assembly Figure 7.4 ANSYS model of tool holder assembly

5 146 The basic dimensions of the model are: Turning insert : 12.7 x 12.7 x 4.7 mm 3 Shim seat : 11.7 x 11.7 x 3 mm 3 Tool holder : 125 x 20 x 20 mm Material Properties The thermal and mechanical properties of the materials used in the model are listed in Table 7.1. Table 7.1 Material properties of cutting tool holder used in finite element thermal model Materials properties Insert and shim seat Tool holder Material carbide AISI 1045 Young s modulus, E (10 3 N/mm 2 ) Poisson s ratio Density (10-9 Kg/mm 3 ) Thermal conductivity k (10-3 w/mm 0 C) Specific heat C - (J/kg 0 C) C C C C C C C C C C C C C C C C Selection of an Element The Element used to build a finite element model of turning tool is SOLID 70. It is used for the 3-D modeling of solid brick element. Eight nodes

6 147 having three degrees of freedom at each node translations in the nodal x, y, and z directions define the element. The special features of the solid 70 element are given below, It has a 3-D thermal conduction capability. The element is applicable to a 3-D, steady-state or transient thermal analysis. It has the surface load of Heat Flux FEA Model of Tool Holder Mesh generation is the process of dividing the analysis continuum into a number of discrete parts of finite elements. In the manually created mesh, it is to be noticed that the elements are smaller at the joint. This is known as mesh refinement, and it enables the stresses to be captured at the geometric discontinuity (the junction). Figure 7.5 Meshed model of tool geometry

7 148 The final mesh consists of 3526 elements and 6562 nodes with refinement of the mesh near the insert as shown in Figure.7.5. The mass and volume of the model is kg and mm 3 respectively Boundary Conditions Boundary conditions used for the model are as follows: The interior boundaries of the insert are in contact with the shim seat and the holder and the surfaces are assumed to be smooth and hold together with a rigid clamping system, hence they are assumed to be in perfect contact. The exterior boundaries of the insert are exposed to the air, except at the tool-chip contact area. For the exterior boundaries of the insert, the shim seat and tool holder boundaries except at back surface, which are exposed to the environment, heat loss due to convection ( h = 20 * 10-6 W/mm 2 0 C) is considered. The far end surface of the tool holder, which is distant from the cutting zone, is assumed to be at room temperature (T 0 = 30 0 C). Initially the whole model is set at room temperature (T 0 ). It is also assumed that the heat intensity is uniformly distributed Estimation of Heat Generation Heat is generated in the primary deformation zone due to plastic work done at the shear plane. Secondly, heat is generated in the secondary

8 149 deformation zone due to work done in deforming the chip and in overcoming the sliding friction at the tool chip interface zone. The rate of energy consumption in metal cutting W c = F v V (7.1) where, F v -cutting force in N V-cutting speed in m/sec Assuming that all the mechanical work done in the machining process is converted into heat, heat generation in the primary deformation zone may be calculated from the work done in the primary deformation zone Heat generation in the primary deformation zone Q p = F v V Watts (7.2) The amount of heat generated due to the work done in the secondary deformation zone along the tool rake face is calculated from the friction energy given by the following equation Amount of heat generated Q s = F fr V / h (7.3) where, F fr - Total shear force acting in the rake face in N F fr = F v sin + F s cos

9 150 where, F v - Cutting force in N F s - Feed force in N - Rake angle in degrees (-5 0 ) V - Cutting speed in m/sec h - Chip thickness ratio Estimation of Cutting Force Design of all kinds of machine tool involving operations with single point tools require analysis of cutting forces at the cutting edge during turning operation. There are several approaches for estimating the cutting forces. An empirical method based on the data of Acadamician Granovsky is described here to facilitate computation of forces in order to aid design work as well as for calculating power consumption. Cutting force acting in the direction of the cutting velocity vector, F z = [C z {K *K *K v *K r *K hf *K cf *K M } z (BHN) m * t p * s q ] kg (7.4) where, C z - material constant for steel = 3.57 (K ) z - co-efficient taking into account the effect of rake angle = 0.92 (K ) z - co-efficient taking into account the effect of principal cutting edge angle= 1.08

10 151 (K v ) z - co-efficient taking into account the effect of cutting speed = 0.91 (K r ) z - co-efficient taking into account the effect of nose radius = 1 (K hf ) z - co-efficient taking into account the effect of tool wear = 1 (K cf ) z - co-eff taking into account the effect of cutting fluid used = 1 (K M ) z - material transfer co-efficient = 1 BHN - brinnel hardness number = 429 m - Exponent of BHN = 0.75 t - Depth of cut = 1.0 p - Exponent of depth of cut = 1.0 s - Feed rate = 0.32 mm/rev q - Exponent of feed = Estimation of Feed Force Feed force (kg) acting in the direction of the tool travel, where, F x =[C x {K *K *K v *K r * K hf * K cf * K M } x (BHN) m * t p * s q ] (7.5) C x - material constant for steel = (K ) x - co-efficient taking into account the effect of rake angle = 0.82 (K ) x - co-efficient taking into account the effect of principal cutting edge angle = 0.7 (K v ) x - co-efficient taking into account the effect of cutting speed = 0.78

11 152 (K r ) x - co-efficient taking into account the effect of nose radius = 1 (K hf ) x - co-efficient taking into account the effect of tool wear = 1 (K cf ) x - co-eff taking into account the effect of cutting fluid used = 1 (K M ) x - material transfer co-efficient = 1 BHN - Brinnel hardness number = 429 m - Exponent of BHN = 1.5 t - Depth of cut = 1.0 p - Exponent of depth of cut = 1.2 s - Feed rate = 0.32 mm/rev q - Exponent of feed = Estimation of Heat Flux The frictional heat flux is estimated by the ratio of the friction power to the real contact area. The tool-chip real contact area significantly affects the frictional heat flux. Cutting Force F z = 3.57 {0.92*1.08*0.91*1*1*1*1} (429) 0.75 x (1.0) 1 x (0.32) 0.75 = N Feed Force F x = {0.82*0.7*0.78*1*1*1*1} (429) 1.5 x (1.0) 1.2 x (0.32) 0.65 = N

12 153 Total Shear Force F fr = *sin (-5 0 ) *cos (-5 0 ) = N Chip thickness ratio h = 1.0/ = Cutting speed V = *D*N/60 in m/sec Where, D - Diameter of the workpiece = m N - Speed of the spindle = 635 rpm V = * * 635 / 60 = m/sec Heat Flux (q fr ) q fr = * / * 16 = W/mm 2 The heat flux values are estimated for varying parameters of cutting speed, feed rate, and depth of cut are listed in the Table 6.2.

13 154 Table 7.2 Heat flux values for various cutting conditions Sl.No Cutting speed (V) in m/min Feed rate (s) in mm/rev Depth of cut (t) in mm Cutting force (Pz) in N Feed force (Px) in N Total shear force(ffr) in N Chip thickness ratio h Heat flux(qfr) in N/mm Finite Element Solution The Finite Element Analysis solver can be logically divided into three main parts, the pre-solver, the mathematical-engine and the post-solver. The pre-solver reads the model created by the pre-processor and formulates the mathematical representation of the model. If the model is correct the solver proceeds to form the element stiffness matrix for the problem and calls the mathematical-engine, which calculates displacement, temperatures, pressures, and output the result. The results are returned to the solver and the post-solver is used to calculate strains, stresses, heat fluxes and velocities for each node within the component. All these results are stored in a results file, which may be read by the post- processor Discussion of Results Simulation of the temperature distribution at the cutting insert for various heat flux values and steady state thermal analysis are carried out by solving the model. For the same input conditions, transient thermal analysis

14 155 are performed with the time period of 1,2,5,10,15,20,25,30 and 35 seconds of the nine steps. The plots for various cutting conditions are plotted from the Figures 7.6 to Figure 7.6 Temperature plot for cutting speed 70 m/min, feed rate 0.32 mm/rev and depth of cut 1.0 mm Figure 7.7 Temperature plot for cutting speed 70 m/min, feed rate 0.32 mm/rev and depth of cut 0.5 mm

15 156 Figure 7.8 Temperature plot for cutting speed 70 m/min, feed rate 0.23 mm/rev and depth of cut 1.0 mm Figure 7.9 Temperature plot for cutting speed 70 m/min, feed rate 0.23 mm/rev and depth of cut 0.5 mm

16 157 Figure 7.10 Temperature plot for cutting speed 44 m/min, feed rate 0.32 mm/rev and depth of cut 1.0 mm Figure 7.11 Temperature plot for cutting speed 44 m/min, feed rate 0.32 mm/rev and depth of cut 0.5 mm

17 158 Figure 7.12 Temperature plot for cutting speed 44 m/min, feed rate 0.23 mm/rev and depth of cut 1.0 mm Figure 7.13 Temperature plot for cutting speed 44 m/min, feed rate 0.23 mm/rev and depth of cut 0.5 mm

18 159 The temperature distributions near cutting edge of insert for the first 35 seconds are tabulated in the Table 7.3 from the transient simulation. Table 7.3 Temperature distributions on cutting tool insert obtained from finite element simulation TEMPERATURE 0 C STEP SIZE TIME, Sec Cutting speed 70 m/min, Feed rate 0.32 mm/rev and Depth of cut 1.0 mm Cutting speed 70 m/min, Feed rate 0.32 mm/rev and Depth of cut.0.5 mm Cutting speed 70 m/min, Feed rate 0.23 mm/rev and Depth of cut 1.0 mm Cutting speed 70 m/min, Feed rate 0.23 mm/rev and Depth of cut 0.5 mm Cutting speed 44 m/min, Feed rate 0.32 mm/rev and Depth of cut 1.0 mm Cutting speed 44 m/min, Feed rate 0.32 mm/rev and Depth of cut 0.5 mm Cutting speed 44 m/min, Feed rate 0.23 mm/rev and Depth of cut 1.0 mm Cutting speed 44 m/min, Feed rate 0.23 mm/rev and Depth of cut 0.5 mm decreases slightly. Maximum temperature is reached at the very early stage and then it Effect of Cutting Variable on Temperature The effect of the factors on the temperature distribution is analyzed using MINITAB factorial design. The estimated effect and the percentage of contribution are given in the Table 7.4.

19 160 Table 7.4 Estimated effect and coefficients for temperature (FEM) Term Effect Coef SE Coef T P Constant Speed Feed rate Depth of cut S = R-Sq = 95.63% R-Sq (adj) = 92.35% From the above analysis, it can be concluded that the depth of cut and cutting speed are the significant factors and the feed rate is not a significant factor. Main Effects Plot (data means) for FEM temp C speed feed rate Mean of FEM t emp C depth of cut Figure 7.14 Effect of main variables on temperature (FEM) The main effect of speed, feed rate and depth of cut for temperature predicted through FEM are shown in Figure From the main plot for temperature, it can be observed that speed, feed rate and the depth of cut are directly proportional to the temperature distribution.

20 EXPERIMENTAL PROCEDURE FOR TEMPERATURE MEASUREMENT This is the most common and simple technique for measuring temperature in the cutting zone. Here, the e.m.f generated between the toolwork interface (hot junction) and their cold ends (cold junction) is taken as the measure of average temperature in the cutting zone. The e.m.f. generated is measured with a millivoltmeter. Both the work and tool should be insulated from the machine tool. There should be a good contact between the two junctions and the measuring instrument. For this, a copper disk is mounted at the free end of the lathe spindle. The circuit is completed with mercury and the tail end of the tool through a mill voltmeter. The magnitude of e.m.f. generated will depend upon the temperature difference between the hot and cold junctions, and nature of tool and workpiece. The measured e.m.f. is converted into temperature with the help of a calibration curve. The picture view of the thermocouple is shown in Figures 7.15 and Table 7.5 shows the numerical values of the various machining parameters (cutting speed, feed and the depth of cut), that have been selected for experimentation and measurement of temperature. The details of workpiece, cutting tool and machine tool are presented in the same table. The inserts are used for one cutting edge of each set of readings and for only one cutting passes of 65 mm each. The workpiece is prepared by removing about 1 mm from the outside surface to eliminate any effect of workpiece surface in homogeneities on the experimental results. A chamfer is also created at the end of the workpiece prior to the actual turning tests to prevent any entry damage to the cutting tool edge at the beginning of the cut.

21 162 Table 7.5 Experimental setup for temperature measurement Machine : MAS CENTER LATHE ACE Designers (Bangalore) Workpiece Materials : OHNS AISI 01 Composition Hardness Size : C=0.90%, Cr=.50%, Mn=1.00%, W=0.5% : 45 HRC : 120 mm length and 30 mm diameter Cutting tool Cutting insert Tool holder : coated Carbide, CNMG (P-30 ISO specification) : PSBNR 2525M12(ISO specification), Working tool geometry : -6,-6, 6,6,15,75 Cutting Condition Cutting Speed Feed Depth of Cut : 1000 rpm, 600 rpm : 0.31 mm/rev, 0.17 mm/rev : 0.5 mm,1.0 mm The temperatures are measured by a stationary digital thermocouple for a calibrated temperature range of C. The thermocouple has been directed to measure the average temperature due to e.m.f generation on the tool rake face. The position is chosen to be on the rake face at 2 mm from the cutting edge and just beside the leading edge in order to avoid the effect of chip obstruction during machining. The position of thermocouple is shown in Figure 7.15

22 163 Figure 7.15 Position of thermocouple on a turning tool Figure 7.16 Thermocouple with stopwatch

23 164 Eight different set of experiments are conducted on the carbide cutting tool to measure temperature. The Table 7.6 shows the experimental values of temperature on insert for different speed, feed and depth of cut Discussion of Results During a cutting process, the force exerted by a motor is converted into heat at a contact area between the tool edge and the chip which diffuses throughout the cutting tool. The local heat process can be classified into three stages: early, transition, and late stages. In the early stage, only the energy transferred is absorbed, resulting in a rapid and temporal increase in the local temperature. In the transition stage, however, the energy transferred is partially absorbed to raise its enthalpy and is then partially diffused into the neighbouring domain. This results in a temporarily exponential increase in the local temperature which then asymptotically approaches its final steady value, i.e. late stage. The variations of tool temperature at the nine locations indicated from step1 through step 9, measured in real-time is presented in Table 7.6. The experimental results reveal that the temperature is varying at the beginning and then reaches the maximum temperature. This deviation is due to heat transfer between the chip and the tool, as heat is taken away from the tool. The effect of the factors on the temperature distribution is analyzed using MINITAB factorial design. The estimated effect and the percentage of contribution are given in the Table 7.7.

24 165 Table 7.6 Temperature distributions on cutting tool insert measured during cutting test Temperature 0 C STEP SIZE TIME, Sec Cutting speed 70 m/min, feed rate 0.32 mm/rev and depth of cut 1.0 mm Cutting speed 70 m/min, feed rate 0.32 mm/rev and depth of cut.0.5 mm Cutting speed 70 m/min, feed rate 0.23 mm/rev and depth of cut 1.0 mm Cutting speed 70 m/min, feed rate 0.23 mm/rev and depth of cut 0.5 mm Cutting speed 44 m/min, feed rate 0.32 mm/rev and depth of cut 1.0 mm Cutting speed 44 m/min, feed rate 0.32 mm/rev and depth of cut 0.5 mm Cutting speed 44 m/min, feed rate 0.23 mm/rev and depth of cut 1.0 mm Cutting speed 44 m/min, feed rate 0.23 mm/rev and depth of cut 0.5 mm Table 7.7 Estimated effect and coefficient for temperature (Experimental) Term Effect Coef SE Coef T P Constant Speed Feed rate Depth of cut S = R-Sq = 84.31% R-Sq (adj) = 72.55%

25 166 From the above table it can be observed that the depth of cut is more a significant factor for temperature on cutting tool, the next significant factor being speed. Main Effects Plot ( data means) for Exp Temp C 170 speed feed rate 160 Mean of Exp Temp C depth of cut Figure 7.17 Main plots for experimental results The main effects of speed, feed rate and depth of cut for experimentally measured temperature are shown in Figure From the main plot it can be observed that speed and the depth of cut are directly proportional to the temperature distribution. 7.5 VALIDATION OF FEM RESULTS WITH EXPERIMENTAL RESULTS The FEM results of temperature distribution are compared with the results that are obtained from the experimental values. These comparative results are shown in the Figures 7.18 to 7.25.

26 167 Figure 7.18 Comparison of Experimental and FEA results for the cutting speed 70 m/min, feed rate 0.32 mm/rev, and depth of cut 1.0 mm Figure 7.19 Comparison of experimental and FEA results for the cutting speed 70 m/min, feed rate 0.32 mm/rev, and depth of cut 0.5 mm

27 168 Figure 7.20 Comparison of experimental and FEA results for the cutting speed 70 m/min, feed rate 0.23 mm/rev, and depth of cut 1.0 mm Figure 7.21 Comparison of experimental and FEA results for the cutting speed 70 m/min, feed rate 0.23 mm/rev, and depth of cut 0.5 mm

28 169 Figure 7.22 Comparison of experimental and FEA results for the cutting speed 44 m/min, feed rate 0.32 mm/rev, and depth of cut 1.0 mm Figure 7.23 Comparison of experimental and FEA results for the cutting speed 44 m/min, feed rate 0.32 mm/rev, and depth of cut 0.5 mm

29 170 Figure 7.24 Comparison of experimental and FEA results for the cutting speed 44 m/min, feed rate 0.23 mm/rev, and depth of cut 1.0 mm Figure 7.25 Comparison of experimental and FEA results for the cutting speed 44 m/min, feed rate 0.23 mm/rev, and depth of cut 0.5 mm.

30 171 The maximum temperature attained during cutting test and finite element methods are tabulated in Table 7.8. The percentage of error for the different cutting conditions is also presented in the same table. Table 7.8 Maximum temperature and percentage of error Sl No Cutting speed (v) m/min Feed rate (s) mm/rev Depth of cut (t), mm Temperature 0 c FEM value Experiment al value Difference bet FEA & exp. Percentage of error The cutting tool insert reaches the max temperature C at high cutting speed, feed rate and depth of cut by finite element method. In cutting test, the maximum temperature is C but the next highest temperature C is obtained at high cutting speed, feed rate and depth of cut. PTFE can with stand the damping property up to C. From the cutting test and FEA results, the maximum temperature of tool reaches only C therefore the damping property of the PTFE coated carbide shim is not affected by temperature and it can with stand this maximum temperature. The cutting test results are similar to the results obtained using FEM. The parameters cutting speed, feed rate and depth of cut for maximum temperature has been monitored. Energy consumption is increased with

31 172 increase in cutting velocity, feed rate and depth of cut. When the energy consumption is greater, the temperature and frictional forces are greater at the tool-chip interface and consequently higher is the tools wears and lower the tool life. The percentages of errors are varying from 2.75 % to %. It is clear that there is not much deviation of temperature between the results of experimental procedures and finite element simulation. The conclusions drawn from this work are summarized as follows: From the cutting test, it is observed that the maximum temperature is C but the next highest temperature, C is obtained at high cutting speed, feed rate and depth of cut. FEA results show that the maximum temperature at the toolchip contact increases with cutting speed but not linearly. The maximum temperature C is obtained at high cutting speed, feed rate and depth of cut. Temperature of the tool is increased mainly due to increase in cutting speed and depth of cut. The average percentage error is 16.5%. It is clear that there is not much deviation of temperature between the results of experimental procedures and finite element simulation. The temperature distribution into the cutting tool for different tool materials and various cutting parameters can be easily assessed using the finite element methods.