Prediction Of Nanocrytalline Microstructure During Machining Of Commercially Pure Titanium

1. Prediction of Nanocrytalline Microstructure During Machining of Commercially Pure Titanium Hongtao Ding, Ph.D. Mechanical Engineering, Purdue University https://engineering.purdue.edu/CLM/

2. Outline of the Contents Introduction Dislocation Density-Based Material Model FE Modeling of Steady-State Orthogonal Cutting Cutting and Grain Refinement Simulations Summery 2

4. Plane-strain orthogonal cutting has recently been exploited as a means to refine the microstructure of metallic materials from tens of micrometers or greater to a few hundred nanometers, e.g., aluminum alloys [1], copper [2-4], nickel-based superalloys [4], steels [4] and titanium [5].

5. Machining only needs a single pass to create large enough strains required for the creation of sub-micron grain sizes in the chips. The plastic strain imposed can be modulated by an appropriate choice of the rake angle of the cutting tool. The material processing rate can also be easily controlled by regulating the cutting speed and/or depth of cut.3

7. The large-strain in the chip formation has been used as a qualitative measure for the grain size change. The effects of cutting speed and workpiece temperature are also very qualitative.

8. A predictive model based on the grain refinement mechanism in machining is critically needed to better design and optimize the process parameters, such as the cutting speed, temperature, depth of cut and tool geometry, etc., for producing the desirable microstructures by machining. 4

9. Dislocation Density-Based Material Model (1) 5 Dislocation density-based material models are useful tools to capture grain size evolution during complex dynamic processes like machining involving multi-process variables Estrinand other researchers [6-9] developed a dislocation density evolution model for equal channel angular processing (ECAP).Their dislocation density-based material model was compatible with the material constitutive models developed under varying conditions of strains, strain rates and temperatures . In the model, a dislocation cell structure is assumed to form during deformation, which consists of two parts, dislocation cell walls and cell interiors, and obeys a rule of mixtures.

11. Thus, in this modeling approach, the theoretically calculated cell size is identified with the grain size.IDB: incidental dislocation boundary GNB: geometrically necessary boundary GB: original grain boundary. 6

13. The second terms denote the transfer of cell interior dislocations to cell walls where they are woven in.

15. 𝑓 volume fraction of the dislocation cell wall

16. ρccell interior dislocation density

17. ρwcell wall dislocation density

18. 𝜌𝑡𝑜𝑡 total dislocation density

20. β*dynamic coefficient of dislocation interaction

21. ko dislocation annihilation parameter

22. m strain rate sensitivity

24. CEL Modeling of Steady-State Orthogonal Cutting A novel coupled Eulerian-Lagrangian (CEL) model is developed to simulate steady-state chip formation and grain refinement in orthogonal cutting. 10 Fig. 3 CEL model setup using ABAQUS/Explicit

25. CEL Model Validation 11 Table 2. CEL model validation test conditions Fig. 5Comparison of predicted cutting force with experiments Fig. 4 Comparison of predicted temperature

26. Material Dislocation Subroutine in CEL model 12

27. Cutting and Dislocation Evolution Simulations 13 Table 3. Orthogonal cutting experiments of CP Ti [5]

28. Video: Cutting and Dislocation Evolution Simulation 14

29. Dislocation Evolution Simulation 15 Fig. 6 Schematic illustration of microstructural evolution during machining. Predicted total dislocation density (1/mm2) Homogeneous, loosely distribution of dislocations in the bulk material Elongated dislocation cell in the chip primary shear zone, with dense dislocations on the cell walls and blocked dislocations by subgrain boundaries Well developed sub-micron grains in the chip, by break up and reorientation of subgrains. TEM micrographs taken in cutting of copper [2]

34. By using the -20° rake angle tool, the induced average shear strain in the chip was more than doubled from the case of 20° rake angle tool. But the average temperature in the chip also increased by about 80-100 °C, which adversely affected the grain refinement due to the increase of dislocations annihilations at a higher temperature.

36. References M.R. Shankar, S. Chandrasekar, A.H. King, W.D. Compton, Microstructure and stability of nanocrystalline aluminum 6061 created by large strain machining, ActaMaterialia, 53 (2005) 4781-4793. H. Ni, A.T. Alpas, Sub-micrometer structures generated during dry machining of copper, Materials Science and Engineering A, 361 (2003) 338-349. S. Shekhar, J. Cai, J. Wang, M.R. Shankar, Multimodal ultrafine grain size distributions from severe plastic deformation at high strain rates, Materials Science and Engineering: A, 527 (2009) 187-191. S. Swaminathan, M.R. Shankar, S. Lee, J. Hwang, A.H. King, R.F. Kezar, B.C. Rao, T.L. Brown, S. Chandrasekar, W.D. Compton, K.P. Trumble, Large strain deformation and ultra-fine grained materials by machining, Materials Science and Engineering: A, 410-411 (2005) 358-363. M.R. Shankar, B.C. Rao, S. Lee, S. Chandrasekar, A.H. King, W.D. Compton, Severe plastic deformation (SPD) of titanium at near-ambient temperature, ActaMaterialia, 54 (2006) 3691-3700. S.C. Baik, Y. Estrin, H.S. Kim, R.J. Hellmig, Dislocation density-based modeling of deformation behavior of aluminium under equal channel angular pressing, Materials Science and Engineering A, 351 (2003) 86-97. S.C. Baik, R.J. Hellmig, Y. Estrin, H.S. Kim, Modeling of deformation behavior of copper under equal channel angular pressing, Zeitschrift fur Metallkunde, 94 (2003) 754-760. S.C. Baik, Y. Estrin, H.S. Kim, H.-T. Jeong, R.J. Hellmig, Calculation of deformation behavior and texture evolution during equal channel angular pressing of IF steel using dislocation based modeling of strain hardening, Materials Science Forum, 408-412 (2002) 697-702. V. Lemiale, Y. Estrin, H.S. Kim, R. O'Donnell, Grain refinement under high strain rate impact: A numerical approach, Computational Materials Science, 48 (2010) 124-132. Hansen, N., Huang, X., and Winther, G., Grain orientation, deformation microstructure and flow stress, Materials Science and Engineering: A, 494 (2008) 61-67. 37