STATUS OF FEM MODELING IN HIGH SPEED CUTTING - A Progress Report - Dr. Taylan Altan, Professor and Director Partchapol (Jay) Sartkulvanich, Graduate Research Associate Ibrahim Al-Zkeri, Graduate Research Associate Engineering Research Center for Net Shape Manufacturing The Ohio State University, Columbus, OH (http://www.ercnsm.org) CIRP- High Performance Cutting Conference, June 12-13, 2006 Vancouver, Canada 1
Current Focus of Research Modeling of Machining Main Research Activities: [1] Determination of Flow Stress Data for Finite Element Simulation [2] Analysis of Burr Formation in Face Milling of a Cast Aluminum Alloy [3] Study of Hard Turning PCBN Tool Edge Preparation Cutting Force and Chip Formation Residual Stresses White Layer [4] Study of Scaling Effects and Tool Coating [5] Prediction of Tool Wear [6] FEM Modeling of Roller Burnishing Process 2
Process Simulation of Cutting Controllable inputs Material Properties Plastic property (Flow stress) Elastic property - Young s modulus - Poisson s ratio Thermal property - Thermal conductivity - Specific heat capacity - Thermal expansion Contact Tribology Friction condition Interface heat transfer Boundary conditions Displacement boundary cond. Thermal boundary cond.... Nature of FEM code (e.g. DEFORM, Third Wave ) Implicit/Explicit Type of elements Meshing/ Remeshing... Outputs Cutting forces Temperatures Chip geometry Surface integrity? Distribution of Stress Strain Strain rate Velocity Uncontrollable inputs Numerical influence Number of remeshing steps Data interpolation algorithm... Neglected Variables Machine Vibration Tool Elastic Deflection Dimensional Tolerances... 3
[1] Determination of Flow Stress Methods Used for Flow Stress Determination High Speed Compression Tests (HSC): Split Hopkinson s Pressure Bar (SHPB): SHPB with Hat-Shaped Sample: Machining Tests (Slot Milling or Orthogonal Turning): -1 ε = 0 to1, & ε = up to 450 s, T = 25 to1,100 C -1 ε = 0 to1, & ε = up to 2,000 s, T = 20 to1,100 C -1 ε = 0 to 0.5, & ε = up to10,000 s, T = 20 to1,000 C -1 ε = 0.5 to1.5, & ε = up to 680,000 s, T = 240 to 900 C Temperature[ C] 100 300 500 700 900 1100 Oxley (high speed compression): temperature: 25...1100 C strain rate: 0...450 s -1 Maekawa (SHPB): temperature: 20...720 C strain rate: 200...2000 s -1 El Magd (SHPB): temperature: 20...600 C strain rate: 0.001...10000 s -1 Example Machining tests: temperature: 240...900 C strain rate: 26,000...680,000s -1 AISI 1045 At strain = 1.0 Conventional compression 0 500 2000 10 4 10 5 10 6 Strain rate [s -1 ] 4
[1] Determination of Flow Stress Slot milling tests Geometrical Relations: Forces in Slot Milling F x : F y : Force in x Direction Force in y Direction F F c t = F = F x x cosθ + F r r sinθ F y y sinθ cosθ r r F t : F c : θ r : Thrust Force Cutting Force Rotation Angle 5
[1] Determination of Flow Stress Force data obtained from slot milling tests Cutting force in X and y direction 1000 [N] 600 400 200 0-200 -400 AISI 1045 X Forces Rake = - 9.06 deg f = 0.1 mm/rev YForces Vc = 300 m/min w = 3 mm uncoated Average of 20 Tool Rotations 0 30 60 90 120 [deg] 180 rotation angle EXP-max EXP-min EXP-AVE 6
[1] Determination of Flow Stress Quick stop milling tests Chip formation Point of Breakage Chip Root Tool δ Workpiece Metal Sheets Tool Rotation Angle rotation feed Φ = Shear angle s = Average plastic zone thickness in primary zone l = Length of shear plane δ = Average plastic zone thickness in secondary zone t 2 = Chip thickness 7
[1] Determination of Flow Stress Flow Stress Determination Using Slot Milling Tests (/) Computational Tasks OXCUT Force Calculation Slot Milling Adjustment for new-iterated flow stress OXCUT Predictions - Cutting and thrust forces (F C, F T ) - Primary deformation zone ratio ( s/l) - Secondary deformation zone ratio (δ/t 2 ) No Is Error minimum? Yes Flow Stress Equation Experimental Tasks Slot Milling Tests Experimental Measurements - Cutting and thrust forces (F C, F T ) - Primary deformation zone ( s/l) - Secondary deformation zone (δ/t 2 ) 8
[1] Determination of Flow Stress Data Determined with the Slot Milling Tests (/) AISI 1018 (126-130 HB) carbon steel AISI 1045 (93-96 HRB) carbon steel AISI 1080 (85-88 HRB) bearing steel AISI 8219 (90-93 HRB) bearing steel P20 (30-32 HRC) mold steel H13 (46-48 HRC) die steel AA 356.0-T6, 7%SiC (59-67 HRB) lost foam casting AA 319 cast aluminum alloy SS 348 stainless steel Copper alloys Inconel 718 Flow Stress Equation Used is a Modified Johnson & Cook s Equation Ti 17 titanium alloy σ = & n ε Bε 1+ C ln 1 1000 T T melt 20 20 m, where B, n, C and m are constants 9
[1] Determination of Flow Stress MAterial DAtabase for Machining Simulation (MADAMS) Summarize flow stress data collected from the literature and from German researchers (in DFG program) in a MS-Access database (Material list: www.ercnsm.org Machining R&D Update Material Database for Machining Simulation (MADAMS)) MADAMS is web-based and accessible to researchers worldwide with a password. MADAMS is established with support from NSF, through Grant # DMI- 9821020 and DMI-0220924. (in cooperation with Universities of Kentucky, Karlsruhe, Aachen, Hannover) 10
[2] Burr Formation in Face Milling of a Cast Al Alloy A Case Study A case study was conducted for face milling of cylinder block and cylinder head surfaces, from cast Aluminum Alloy A356-T6. Objectives are to evaluate/compare tool performance and to analyze burr formation based on tool edge and flank wear geometries. Tool rotation Tool Motion and Burr Locations in Face Milling [Arai, 1987] 11
[2] Burr Formation in Face Milling of a Cast Al Alloy A Case Study Flow Stress Equation (through slot-milling tests): σ = 477ε 0.144 & ε T 20 1+ 0.0067 ln 1 1000 585 20 Friction Assumption: Shear friction law with m = 0.6 1.62 for 0.7 ε 20,000 & ε 50 T 1.2 500,000 s o 350 C 1 Chip Formation Simulation using KontiSpan* *Developed at RWTH-Aachen T T Simulation of Burr Formation T T T k n = h ( T T ) T k n = h int ( T ) T c T k n = h ( T T ) T Auto-feed V C T k = 0 n Auto-cut T k n = h ( T T ) V C T k = 0 n T T 12
[2] Burr Formation in Face Milling of a Cast Al Alloy A Case Study Workpiece: Tool: Cutting condition: AA356-T6 Aluminum alloy (casting). PCD, Rake angle = 0 deg. Edge radius = 0.01 mm. Cutting speed =1433 m/min, feed rate = 0.25 mm/rev. 13
[2] Burr Formation in Face Milling of a Cast Al Alloy A Case Study The Effect of Rake Angle and Flank Wear Worn insert generates 39% more burr area than the sharp insert. Additional positive rake angle is effective for reducing burr at both sharp and worn-out conditions. Tool fracture may occur at large positive rake angles Large positive rake angle is preferable if the tool life is reached by flank wear rather than fracture at the tool tip. Burr area (mm 2 ) 0.3 0.2 0.1 Sharp Rake angle = 0 degree Rake angle = 20 degree Worn-out 0.0 0 0.1 0.2 0.3 0.4 0.5 Flank Wear Width (VB, mm) 14
[2] Burr Formation in Face Milling of a Cast Al Alloy A Case Study Evaluation of the Performance of Variable Edge Honed Tool A procedure was established to emulate 3D face milling with a simplified 2D orthogonal cutting model Tool cross sections in the chip flow direction are considered for 2D simulations. Tool with Uniform Edge Hone Radius C Tool with Variable Edge Hone Radius C A B A B Tool Section Effective Uncut Chip Thickness at 90 rotation angle (mm) Tool Edge Radius (µm) Uniform Edge Honed Tool Variable Edge Honed Tool A-A 0.042 25.4 4.2 B-B 0.200 25.4 20.0 C-C 0.254 25.4 25.4 15
[2] Burr Formation in Face Milling of a Cast Al Alloy A Case Study 3D Face Milling Simulations Two 3D face-milling simulations were conducted for the tools at sharp and worn-out conditions. Workpiece was assumed as a small section near the exit where the maximum uncut chip load is located. High mesh density was defined for regions near the tool corner radius and at the exit of burr. For face milling simulation of the worn insert, experimental flank wear geometries were included in a solid model of the insert. 3D View Tool rotation Tool feed Tool Workpiece 16
[2] Burr Formation in Face Milling of a Cast Al Alloy A Case Study 3D Face Milling Simulations Definition of burr geometry Comparison of burr profile between 3D (side view) and 2D simulations Burr height Burr width Burr thickness Burr height (mm) 0.10 0.08 0.06 0.04 0.02 0.00 3D Face Milling Simulation 2D Orthogonal Cutting Simulation -0.6-0.4-0.2 0.0 Burr thickness (mm) Cutting plane for a side view Comparison of burr profile between milling with sharp and worn inserts Burr height (mm) Milling with sharp insert Milling with worn insert 0.3 0.2 0.1-0.6-0.5-0.4-0.3-0.2-0.1 0 0.1-0.1 Burr thickness (mm) 0 17
[2] Burr Formation in Face Milling of a Cast Al Alloy A Case Study 3D Face Milling Simulations Effective Strain Possible fracture Highly stretching region indicates possible fracture 18
[2] Burr Formation in Face Milling of a Cast Al Alloy A Case Study Summary of the Findings/ Recommendations Tool insert with larger flank wear width generates more burrs. Larger positive rake angle (within limits) is desirable for burr reduction if the tool life is determined by flank wear rather than tool fracture. There is no significant difference in burr formation between uniform and variable edge honed tools. However, variable edge honed tool indicates potential increase in tool life and slowing down of burr generation rate. Reduction of the wiper length of the insert can contribute to burr reduction. The predicted burrs from 3D simulation are much smaller than the burrs seen in experiments. The difference could be caused by the accumulation of burrs as result of several subsequent cutting passes. Unless burrs fracture, the larger number of repetitive cutting passes can result in larger burrs. 19
[3] PCBN Tool Edge Preparation in Hard Turning A Case Study Introduction Goal: Predicting the best PCBN tool edge preparation Challenges of FEM Model chip formation accurately: Be able to model serrated chip formation Accurate flow-stress and friction models: Can provide the results close to experiments 20
[3] PCBN Tool Edge Preparation in Hard Turning A Case Study Tool, Workpiece and Cutting Condition Tool insert TPG-432T, mounted on CTFPL-163D tool-holder Chamfer angle (γ): 15 to 30 deg Edge hone (r): 5 to 20 micron Rake angle (α): 5 deg Clearance angle (δ): 6 deg Chamfer width (w): 0.2 mm PCBN Grade: Low CBN Work Material: 100Cr6 (AISI E52100) 60 HRC, Through-hardened Cutting Condition Cutting speed (V c ): 91 to 182 m/min Feed rate (f): 0.076 and 0.152 mm/rev Width of cut: 3.16 mm f Workpiece Cutting Tool V c 21
[3] PCBN Tool Edge Preparation in Hard Turning A Case Study n & ε T T r σ = ( A + B ε ) 1+ C ln 1 & ε 0 Tm Tr m ( ) ( ) ln( & ) ( ) σ = ε + + ε + ε n C F HRC G HRC D A B T m Flow stress model for AISI 52100 from machining (Huang, 2002) and compression tests (Umbrello, 2004) Huang s model (for ranges: ε > 1.0, ε& > 10 4, T = 300 to 500 C): ε Umbrello s model (for ranges: 1.0, 10 2, T = 20 to 300 C and 500 to 1200 C): ε& No inherent surface-crack initiation/propagation mechanism in the cutting simulation Friction Condition (Zorev s model / m = 1, µ = 0.35) 22
[3] PCBN Tool Edge Preparation in Hard Turning A Case Study Serrated chip formation and mesh distribution in FEM simulation of hard turning Temperature distributions during hard turning 23
[3] PCBN Tool Edge Preparation in Hard Turning A Case Study Summary of the Findings The predicted (FEM) and measured forces are close (20% and 9% in predicting cutting and thrust forces, respectively). Their variations with chamfer angle and cutting speed are predicted. The tool chamfer angle has the largest influence on thrust forces. Although similar chip morphology (i.e. serrated chip) is predicted, the exact dimensions of predicted serrated chips do not match the experiment. Cyclic (16% more than average) Von-Mises stresses are generated due to the serrated chipping. Simulations must be improved to predict the residual stresses. 24
Current Status of FEM Modeling of Cutting FEM Models of Cutting have been developed for various studies and applications (e.g. force prediction, chip formation, tool stress, burr formation, tool wear, tool coating, etc.) Although several material testing methods have been proposed, the determination of flow stress data is not satisfactory. Cutting force and chip thickness can be well predicted with FEM. However, predicted thrust force from FEM is always lower than the experiments, especially for positive rake tool. FEM is good for predicting trends but needs further improvements for quantitative predictions of Tool-chip contact length and consequent crater wear. Onset of serrated chip formation and serration frequency Residual stresses in machined surface Most FEM studies have focused on 2D orthogonal cutting. 25
Major Challenges for Practical Application of FEM in Machining Robust methodology for determining flow stress (inverse analysis using cutting tests / determination of properties of surface layers) Prediction of tool wear and residual stresses (elastic deformation of workpiece, tool and machine / microstructural variations on machined surface) Parallel processing for practical use of 3D FEM modeling (partially achieved) Others? 26
Animations of FEM Process Simulations FEM Analysis of 3-D Turning Process (2 of 2) Material: AISI 8219 ( flow stress model) Cutting insert: TNMA432 uncoated K68 carbide Cutting speed: 320 m/min Feed rate: 0.25 mm/rev Depth of cut: 1.5 mm Normal rake angle: -7 deg Inclination angle: -7 deg Lead angle: 0 deg Effective stress Mesh model 27
Animations of FEM Process Simulations 2-D Simulation of Serrated Chip Formation in HSC Workpiece Material: AISI 1045 (Oxley) Edge Radius: 0.1 mm Tool Material: Uncoated Carbide Rake Angle: -7 deg, Depth of Cut: 1 mm Cutting Speed: 150 m/min Fracture criterion: Normalized C & L 650 ºC 20 ºC 28
Questions?? 29