外文翻译--物理上一种仿真模型纯钛合金正交切削的分段芯片的形成 英文版.pdf

A physically based constitutive model for simulation of segmented chip formation in orthogonal cutting of commercially pure titanium Shreyes N. Melkote (2)a, *, Rui Liu a, Patxi Fernandez-Zelaia a, Troy Marusich b aGeorge W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA bThird Wave Systems LLC, Minneapolis, MN, USA 1. Introduction A segmented chip is commonly observed in cutting materials with low thermal conductivity (e.g. titanium and its alloys). The low thermal conductivity gives rise to heat accumulation in the primary shear zone, which causes localized softening, and shear localization and chip segmentation 1. This in turn can cause undesirable oscillations in the cutting force and associated vibrations that can inhibit tool life and yield poor surface quality and dimensional accuracy of the machined feature. Segmented chip formation has been simulated by several researchers using different modelling approaches that are well- documented in a recent CIRP keynote paper 2. Based on observations of voids and cracks in the shear band, Uhlmann et al. 3 simulated segmented chip formation by incorporating a ductile fracture mechanism in their model. Hua and Shivpuri 4 used an energy-based ductile fracture criterion to simulate segmented chip formation in cutting of Ti-6Al-4V. More recently, Calamaz et al. 5 proposed a phenomenological modifi cation to the popular Johnson-Cook fl ow stress model to simulate chip segmentation in cutting of Ti-6Al-4V. This model was further refi ned by Ozel and his co-workers for different applications 6,7. Rotella and Umbrello 8 used a similar fl ow stress model in conjunction with evolution equations for grain size and hardness variation to predict microstructure changes due to dynamic recrystallization (DRX) in dry and cryogenic machining of Ti-6Al-4V. Calamaz et al. 5 also noted that strain softening can be attributed to microstructural changes induced by dynamic recovery (DRV) and/or DRX processes active during severe plastic deformation. Ding and Shin 9 presented a physically based material model that accounts for these effects using dislocation density as the sole internal state variable. However, they only simulated continuous chip formation with their model. In this paper, a recently developed physically based constitutive model 10, which is motivated by the mechanics of interaction of mobile dislocations with microstructural barriers, is extended by incorporating an additional deformation mechanism to allow for accurate simulation of segmented chip formation in cutting of commercially pure titanium (CP-Ti). Specifi cally, in order to describe the material constitutive behaviour when ultrafi ne grains are formed in the shear band during cutting of CP-Ti, the inverse HallPetch effect (IHPE), commonly attributed to grain boundary sliding 11, is introduced in the model. This enables the material model to capture fl ow softening below a critical grain size. The model is implemented as a user-defi ned subroutine in a FEM- based machining simulation software AdvantEdgeTM(Third Wave Systems, USA) and is used to simulate orthogonal cutting of CP-Ti. Orthogonal cutting experiments are conducted to determine the cutting forces and chip characteristics, which are compared with simulation results to evaluate the performance of the enhanced model. 2. Physically based constitutive model This section briefl y summarizes key aspects of the previously developed constitutive model 10, which deals with simulation of continuous chip formation, and discusses the IHPE model enhancement. Following thermal activation theory 12, the fl ow strength of a metal undergoing plastic deformation is formulated as a linear superposition of an athermal stress, sa, a thermal stress, sth, and a dislocation drag stress, sd, as follows: s sa sth sd(1) CIRP Annals - Manufacturing Technology xxx (2015) xxxxxx A R T I C L E I N F O Keywords: Machining Modelling Segmented chip A B S T R A C T The accuracy of cutting simulations depends on the knowledge of micro-scale physics included in the constitutive and microstructure evolution models of the cutting process. This paper presents an enhanced physically based material model that accounts for microstructure evolution induced fl ow softening due to the inverse HallPetch effect below a critical grain size. The models ability to simulate segmented chip formation and grain refi nement in the shear bands produced in orthogonal cutting of commercially pure titanium is evaluated through fi nite element simulations and experiments. Results show good prediction accuracy for the cutting and thrust forces, chip morphology, and segmentation frequency. ? 2015 CIRP. * Corresponding author. E-mail address: (S.N. Melkote). G Model CIRP-1327; No. of Pages 4 Please cite this article in press as: Melkote SN, et al. A physically based constitutive model for simulation of segmented chip formation in orthogonal cutting of commercially pure titanium. CIRP Annals - Manufacturing Technology (2015), /10.1016/ j.cirp.2015.04.060 Contents lists available at ScienceDirect CIRP Annals - Manufacturing Technology journal homepage: /cirp/default.asp /10.1016/j.cirp.2015.04.060 0007-8506/? 2015 CIRP. The magnitude of sthdepends on the strength of interac- tions of mobile dislocations with short-range barriers such as lattice friction and solute atoms. This component is modelled using the formulation proposed by Mecking and Kocks 13 as follows: sth 1 ? kT g0mb3 ln e0 e ? ? !1=q 2 4 3 5 1= p s0(2) where, k is the Boltzmanns constant, T is the absolute temperature, g0is the normalized activation energy at 0 K, m is the temperature- dependent shear modulus, b is the magnitude of the Burgers vector, e0is a reference strain rate, s0is the stress required to overcome short range obstacles at 0 K, and p and q are parameters defi ning the shape of energy barriers associated with short range obstacles. The athermal stress, sa, is formulated as the sum of stresses required to overcome the resistance to dislocation motion offered by grain boundaries, sG, and dislocation forests, sr, as follows: sa sG sr aGm ffi ffi ffi b p ffi ffi ffiffi D p armb ffi ffi ffiffi r p (3) where aGand arare parameters related to the strength of mobile dislocation-grain boundary and dislocation-dislocation forest interactions, respectively. The two internal state variables, dislocation density, r, and average grain size, D, evolve with deformation. In the grain boundary contribution term, sGin Eq. (3), the parameter aGequals a constant, a0 G, which is independent of grain size in the deformation regime where the conventional HallPetch effect is active. Below a critical grain size (Dcr), which is a function of temperature, the IHPE is observed in many metals and is accompanied by a decrease in fl ow stress with decreasing grain size (Fig. 1). In order to capture this softening, aGis modelled using the phenomenological equation: aG a0 Gtan h d D?0:5 ? ?v (4) where d and v are temperature-dependent parameters with forms given in Table 2. For this combination of d and v, the resultant Dcrat room temperature is ?10 nm, which is in agreement with values reported for various metals 14. Evolution (refi nement) of grain size, D, due to continuous DRX, which occurs in severe plastic deformation of titanium 15, is modelled as follows: D Df D0? Dftan h er e ? ?u (5) where erand u are temperature and strain rate dependent parameters, Dois the initial grain size, and Dfis the fi nal recrystallized grain size defi ned as a function of the Zener- Hollomon parameter, Z eexpQ=RT, as follows: Df CzZ?m(6) where Czand m are material dependent parameters. In the term representing the contribution of dislocation forests, sr, the evolution of dislocation density is modelled as follows: r rR rH No. of Pages 4 Please cite this article in press as: Melkote SN, et al. A physically based constitutive model for simulation of segmented chip formation in orthogonal cutting of commercially pure titanium. CIRP Annals - Manufacturing Technology (2015), /10.1016/ j.cirp.2015.04.060 equiaxed a-phase grains of average diameter 40 mm. To ensure plane strain conditions, the tube wall thickness was restricted to 2 mm. To probe a wide range of strains and strain rates, three feeds (tu= 0.1, 0.2, 0.3 mm) and fi ve cutting speeds (vc= 20, 60, 100, 140, 180 m/min) were used. Each test condition was replicated twice. Also, each test was conducted with a 08 rake angle tool and a new uncoated tungsten carbide insert (Kennametal TCMW3251, KCK20) with an up-sharp cutting edge (?10 mm). No cutting fl uid was used. The cutting force, Fc, and thrust force, Ft, were measured using a piezoelectric force dynamometer (Kistler Model 9257B). The cut chips were cold mounted in epoxy, ground and polished to a 0.05 mm fi nish. Krolls reagent, a mixture of 1 ml hydrofl uoric acid (HF, 40%), 2 ml nitric acid (HNO3, 40%) and 247 ml de-ionized water, was used to etch and reveal the chip microstructure. 5. Finite element model To simulate orthogonal cutting, a 2-D fi nite element model was built in AdvantEdgeTM(Third Wave Systems, USA), a physics-based machining simulation code. The enhanced constitutive model presented earlier was implemented in the software via a user- defi ned yield surface routine coded in FORTRAN. The contact condition at the tool/chip interface was modelled using the Coulomb friction law. The mean coeffi cient of friction, b, at the tool/chip interface for each simulation (listed in Table 3) was computed from the measured Fcand Ftand the equation b = (Ft+ Fctana)/(Fc? Fttana), where a is the rake angle. 6. Results The simulated Fcand Ftare compared with experimental results in Fig. 2. The measured Fcand Ftdecrease with increasing vcand decreasing tu. The simulated results show a similar trend with less than 5% prediction error for Fcand 1020% error for Ft. The higher error in Ftis attributed to the simple Coulomb friction model used and the absence of tool wear in the fi nite element model, which is invariably present when cutting titanium. The simulated peak (S1) and valley (S2) thicknesses of the segmented chips (see Fig. 4) are compared with measurements in Fig. 3. Note that only fully formed shear bands were included in the measurements. Generally, the measured chip thickness decreases with increasing vcand decreasing tu. The simulated values show a similar trend but tend to overestimate the thickness, especially S2, for most of the cutting conditions. One reason for this is the lack of a ductile fracture mechanism in the model to account for cracks often observed at the free surface of the chip in the vicinity of the shear band (see Fig. 6(a). Detailed comparisons of the chip morphology are shown in Fig. 4. It can be seen that the major features of segmented chips are captured by the simulations. Table 3 Friction coeffi cients used in the simulations. tu= 0.1 mm tu= 0.2 mm tu= 0.3 mm vc= 20 m/min 0.63 0.50 0.44 vc= 60 m/min 0.52 0.40 0.34 vc= 100 m/min 0.49 0.37 0.30 vc= 140 m/min 0.46 0.31 0.28 vc= 180 m/min 0.42 0.29 0.27 Fig. 4. Comparison of measured and simulated chip shapes. Fig. 3. Mean and variation of experimental (EXP) and mean of simulated (SIM) peak (S1) and valley (S2) thicknesses of the machined chip for different cutting conditions. Note: due to irregularity in chip geometry, the mean estimator and mean estimator variance of S1 and S2 were calculated using a bootstrapping method applied to 1015 data points per chip. Fig. 2. Mean and variation of experimental (EXP) and mean of simulated (SIM) cutting (Fc) and thrust (Ft) forces for different cutting conditions. S.N. Melkote et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxxxxx 3 G Model CIRP-1327; No. of Pages 4 Please cite this article in press as: Melkote SN, et al. A physically based constitutive model for simulation of segmented chip formation in orthogonal cutting of commercially pure titanium. CIRP Annals - Manufacturing Technology (2015), /10.1016/ j.cirp.2015.04.060 Fig. 5 shows a comparison of the experimental and simulated chip segmentation frequencies calculated from the mean peak-to- peak distance and the cutting speed. The simulation results capture the measured trends, which show that the segmentation frequency increases almost linearly with vcand decreases with increasing tu. In order to evaluate the ability of the model to qualitatively predict the microstructure in the machined chip, four specifi c locations in the chip, marked A to D in Fig. 6(a), are selected. Position A is located away from the shear band, B is on the boundary, C is inside the shear band, and D is at the tip of the shear band. The corresponding distributions of grain size and dislocation density in the four locations are shown in Fig. 6(b and c). In location A (see Fig. 6(a), the grain refi nement is small. The simulation in Fig. 6(b) also shows a less refi ned grain size at A due to the lower plastic strain in this region (see Fig. 4). Nevertheless, the strain is suffi cient to cause an increase in dislocation density compared to the initial value (see Fig. 6(c). The somewhat refi ned grain structure in location B (Fig. 6(a) suggests that suffi ciently large plastic deformation occurred here. The corresponding simulation in Fig. 6(b) also indicates a more refi ned grain size in this region. Inside the shear band (location C), the simulation yields an ultrafi ne grain size (Fig. 6(b) due to the DRX and IHPE mechanisms in the model. The predicted average grain size is 5070 nm in the shear band region. At elevated temperatures in the shear zone, the ultrafi ne grains give rise to the inverse Hall Petch effect, which causes softening of the material. Additionally, lower dislocation density (compared to A and B) is predicted in the shear band (Fig. 6(c), which is consistent with the ultrafi ne grain size seen in Fig. 6(b). The lower dislocation density in the shear band can be explained by the dislocation annihilation/consump- tion processes active during DRX, consistent with the known physics of DRX 16. Note that small ductile cracks are often observed at location D, as seen in Fig. 6(a). Because loss of material strength due to ductile fracture is not included in the model, the simulation is unable to reproduce this observation. 7. Conclusions The paper presented an enhanced physics-based constitutive model for simulation of segmented chips formed in cutting of commercially pure titanium (CP-Ti). The model incorporates the inverse HallPetch effect (IHPE) to describe the softening effect of ultrafi ne grain structure in the shear band on the material fl ow strength. The fl ow strength is an explicit function of the grain size and dislocation density, which evolve with deformation. In order to validate the simulation results, orthogonal cutting experiments were performed over a range of feeds and speeds. The model simulations yield reasonably accurate predictions of the cutting force (?5% error), thrust force (1020% error), segmentation frequency, and chip morphology. Additionally, the model is able to simulate the spatial distribution of grain size and dislocation density, which are shown to be in good qualitative agreement with the observed chip microstructure. Future work will focus on adding a ductile fracture mechanism to the model to capture crack formation in the shear band region. References 1 Komanduri R, Hou ZB (2002) On Thermoplastic Shear Instability in the Machining of a Titanium Alloy (Ti-6Al-4V). Metallurgical and Materials Trans- actions A 33(9):29953010. 2 Arrazola PJ, Ozel T, Umbrello D, Davies M, Jawahir IS (2013) Recent Advances in Modelling of Metal Machining Processes. CIRP Annals Manufacturing Tech- nology 62(2):695718. 3 Uhlmann E, von der Schulenburg MG, Zettier R (2007) Finite Element Modeling and Cutting Simulation of Inconel 718. CIRP Annals Manufacturing Technology 56(1):6164. 4 Hua J, Shivpuri R (2004) Prediction of Chip Morphology and Segmentation during the Machining of Titanium Alloys. Journal of Materials Processing Technology 150(1):124133. 5 Calamaz M, Coupard D, Girot F (2008) A New Material Model for 2D Numerical Simulation of Serrated Chip Formation when Machining Titanium Alloy Ti- 6Al-4V. International Journal of Machine Tools and Manufacture 48(3):275288. 6 Ozel T, Thepsonthi T, Ulutan D, Kaftanog lu B (2011) Experiments and Finite Element Simulations on Micro-milling of Ti-6Al-4V Alloy with Uncoated and cBN Coated Micro-tools. CIRP Annals Manufacturing Technology 60(1):8588. 7 Ozel T, Ulutan D (2012) Prediction of Machining Induced Residual Stresses in Turning of Titanium and Nickel Based Alloys with Experiments and Finite Element Simulations. CIRP Annals Manufacturing Technology 61(1):547550. 8 Rotella G, Umbrello D (2014) Finite Element Modeling of Microstructural Changes in Dry and Cryogenic Cutting of Ti6Al4V Alloy. CIRP Annals Manufacturing Technology 63(1):6972. 9 Ding H, Shin YC (2014) Dislocation Density-based Grain Refi nement Modeling of Orthogonal Cutting of Titanium. Journal of Manufacturing Science and Engineering 136(4):041003. 10 Liu R, Salahshoor M, Melkote SN, Marusich T (2014) A Unifi ed Material Model Including Dislocation Drag and its Application to Simulation of Orthogonal Cutting of OFHC Copper. Journal of Materials Processing Technology 216:328 338. 11 Chokshi AH, Rosen A, Karch J, Gleiter H (1989) On the Validity of the Hall Petch Relationship in Nanocrystalline Materials. Scripta Metallurgica 23(10):16791683. 12 Kocks UF, Argon AS, Ashby MF (1975) Thermodynamics and Kinetics of Slip. Progress in Materials Science 19:139143. 13 Mecking H, Kocks UF (1981) Kinetics of Flow and Strai