1 Comparison between Experimental & Numerical Results for Single Point Diamond Turning (SPDT) of silicon carbide (SiC) John Patten, Director Manufacturing Research Center Western Michigan University NAMRC 35 May 22, 2007

2 Agenda Introduction to Silicon Carbide (SiC) Background of HPPT Research Background of ceramic simulations 2-D orthogonal machining simulations –Simulations of edge turning –Simulation of plunge cutting –Simulations of fly-cutting 3-D scratching simulations –Silicon Carbide Summary of results Conclusions and future work

3 Silicon Carbide – Advanced Engineering Ceramic Types of SiC Properties and applications of SiC Problems in manufacturing

4 Research background – HPPT of ceramics Define HPPT HPPT or amorphization of ceramics is responsible for the ductile behavior of these brittle materials. HPPT has been identified in Si and Ge, and other materials. Ductile material removal has been achieved in SiC under nanometer cutting conditions and phase transformation of chips has been recorded. Some factors contributing to ductile material removal at room temperature –machining depth < t c –negative rake angle tools with small clearance –sharp edge radius

5 Developments in simulations of ceramic machining Introduce AdvantEdge Developments in AdvantEdge –2-D simulations of Silicon Carbide in the nanometer regime –2-D simulations of Silicon Carbide using DP model –Newly developed 3-D scratching simulation capability Other developments outside AdvantEdge –FEA simulation of polycrystalline alpha-SiC –MD simulations of nanoindentation in SiC

6 2-D orthogonal machining simulations of SiC Three types of experiments were simulated –Edge turning of SiC –Plunge cutting of SiC –Fly-cutting of SiC Visualization of 3-D turning operation in 2-D

7 Typical setup for 2-D orthogonal simulations ParametersGeometry Tool Cutting edge radius, r Rake angle, α Clearance angle, β Workpiece Workpiece length, l Workpiece height, h Process Depth of Cut, feed Length of Cut, loc Cutting Speed, v Width of cut Coefficient of friction

8 Material model for simulations of SiC The DP yield criterion is given by κ is given by J 2 is given by For a uniaxial state of stress Thus J 2 is given by This gives κ of GPa and α of Here, σ t = H/2.2 and σ c = H For H=26 GPa, κ becomes GPa.

9 Simulations of edge turning

10 Edge turning simulations of SiC Variable Definition Value Workpiece-Tool geometry tool cutting edge radius 50 nm tool rake angle 0º & -45º tool clearance angle 5º & 50º In-feed/uncut chip thickness (50, 100, 250, 300, 500) nm work piece velocity 0.05 m/s Width of cut 250 µm

11 Simulation with achieved depth of approx. 220 nm Note the deflection of workpiece material

12 Results from edge turning simulations, 0º rake, 5º clearance

13 Results from edge turning simulations, 0º rake, 5º clearance

14 Results from edge turning simulations, -45º rake, 5º clearance

15 Results from edge turning simulations, -45º rake, 5º clearance

16 Simulations of plunge cutting experiments

17 2-D plunge cutting simulations of SiC Using a flat nose tool, machining was performed across the wall thickness of a tube of polycrystalline SiC.

18 Parameters for plunge-cutting simulations of SiC ParametersValueUnitGeometry Tool Cutting edge radius, r50.0nm Rake angle, α-45.0deg Clearance angle, β11 & 0deg Workpiece & Process Workpiece length, l3.0µmµm Workpiece height, h1.0µmµm (Actual ) Depth of Cut, doc24.0nm Width of cut3.0mm Length of Cut, loc2.0µmµm Cutting Speed, v5.0m/s coefficient of friction, COF0.1-

19 Simulation with achieved depth of 25 nm Note the deflection of workpiece material

20 Results from simulations of SiC

21 Flycutting experiment

22 Flycutting experiment setup

23 Force results from flycutting of SiC 4 distinct cuts made First cut overlapped 6 times Significant noise generated towards end of the experiment

24 Results from flycutting of SiC

25 Results from cut 1, cut 2 & cut 3

26 Results from cut 4

27 Simulations of flycutting

28 Simulations of flycutting experiments ParametersValueUnitGeometry Tool Cutting edge radius, r40.0nm Rake angle, α-45.0deg Clearance angle, β5deg Workpiece & Process Workpiece length, l20.0µmµm Workpiece height, h7.5µmµm In-Feed, feed61 & 75nm Length of Cut, loc15.0µmµm Cutting Speed, v0.518m/s Friction factor0.1- Method A Method B

29 Results of simulations, Method A

30 Results of simulations, Method B

31 3-D scratching simulations

32 3-D scratching simulations

33 Setup for 3-D scratching simulation of SiC ParametersValueUnitGeometry Programmed Depth (feed)125nm Actual depth, doc103nm Length of Cut, loc10.0µmµm Cutting Speed, v0.305mm/s Friction factor, µ0.1, 0.26, 0.6-

34 Scratching simulations of SiC

35 Results from simulation of SiC

36 Summary of results Summary of 2-D orthogonal machining simulations –Simulations agree with experiments for depths close to 100 nm and below. –Pressures at the tool-workpiece interface are greater than the hardness of the material for these depths. –Workpiece deflection leads to actual depth being smaller than the programmed depth. Summary of 3-D scratching simulations –SiC simulations show thrust forces in good agreement with the experiment. –SiC simulations show cutting forces that are not in very good agreement with the experiment.

37 Conclusions Two types of simulations have been presented: 2-D & 3-D –2-D orthogonal simulations of SiC produce useful results for depths at or below the DBT depth of the material. –2-D simulations create pressures at the tool-workpiece interface that are in agreement with what is expected from the experiments. –3-D scratching work shows encouraging results for initial attempts at simulations of ceramic materials for depths below the DBT depth of the materials.

38 Recent Related Work Validation of material models. Development of analytical model to predict actual depth of cut for a programmed depth of cut for each material Predicting behavior of ceramic materials under the brittle mode. 2-D flycutting simulation using VAM. Current effort: 3-D turning simulations using round nose cutting tools.

39 Acknowledgements National Science Foundation for the research grant Andy Grevstad and Third Wave Systems for software support. Jeremiah Couey and Dr Eric Marsh at Penn State University. Dr Guichelaar for equipment at the Tribology lab. Lei Dong at University of North Carolina at Charlotte.

40 Questions and suggestions

41

42 Results from edge turning simulations, -45º rake, 50º clearance

43 Scratching simulation of Si

44 Results from scratching simulation of Si