Advances in Direct Metal Deposition

Advances in Direct Metal Deposition Jyoti Mazumder* and Lijun Song University of Michigan July 15th, 21 Presented By S H Lee *Robert H Lurie Professor of Engineering A Laser Workshop on Laser Based Manufacturing

Outline Background History of DMD Introduction DMD System Overview Advances in DMD System Modeling Geometry Control Temperature Control Composition Prediction Microstructure Prediction Summary A Laser Workshop on Laser Based Manufacturing

Rapid Prototype Customer Initial CAD Model Conceptual Design Assembly Layouts Reduction in Time and Step Detailed Design SFM Prototype Production Time A Laser Workshop on Laser Based Manufacturing

Additive Manufacturing Additive Process Originally for Rapid Prototyping Application 3-D CAD data -> 2-D Slicing Layer by Layer Build-up Low Volume Manufacturing A Laser Workshop on Laser Based Manufacturing

Major RP Techniques StereoLithography (by 3D System) Laminated Object Manufacturing (by Helisys) Selective Laser Sintering (by DTM - 3D System) Fused Deposition Modeling (by StrataSys) 3D Printing (by Z-Corp.) Etc. Solid Ground Curing, Solder, Light Sculpting, Droplet Based Manufacturing, Holographic Interference Solidification,... A Laser Workshop on Laser Based Manufacturing

New Concept in RP Rapid Prototyping Intermediate Step Design Support or Verification Plastic Mold Part, Cast Mold Pattern or Mock-up Part Rapid Manufacturing/Production Near Net Shaped Product or Prototype (accuracy and finish) Functional Part (mold and metallic part) Better Part (multi-materials, heterogeneous) A Laser Workshop on Laser Based Manufacturing

Metal Deposition - Metallic Additive Manufacturing Laser sintering (LS) based Selective Laser Sintering (U of Texas, DTM, 1996) - 3D Systems Direct Metal Laser Sintering (EOS, Germany) Laser cladding (LC) based Direct Metal Deposition (UIUC, 1993) - UM/POM Laser Engineered Net Shaping (Sandia NL, 1996) - Optomec Direct Light Fabrication (Los Alamos NL, 1996) Controlled Metal Buildup (Fraunhofer, Aachen, 1996) Droplet based Droplet Based Manufacturing (UCI, 1991; MIT, 1993) A Laser Workshop on Laser Based Manufacturing

The Vision Imagine a global society where: scientists in Ann Arbor and Aachen, in the security of their laboratories, are analyzing, sharing and using experimental data, a global design team is collaboratively creating a new product and submitting it for fabrication to the company facility in Shanghai. Global collaboration for innovation over the Internet will cross-pollinate ideas, cut travel and reduce costs. A multi-national company designing their product in Detroit and producing in Dalian, China If the part is big, take process to the part Product on order anywhere any time A Laser Workshop on Laser Based Manufacturing

Part on Order Anywhere A Laser Workshop on Laser Based Manufacturing

Running to Moon: Mold & Mirrors.5 mm wall thickness in steel A Laser Polished Workshop to 4 on Laser Based Manufacturing Angstroms!

Application In Tissue engineering Titanium scaffold for implantation study in a mice spinal column * Image Provided by Prof. Scott Hollister 5 mm X-Ray of the Ti-Scaffold After Subcutenous Bone Growth Ti~ Bright White Bone ~ Blue Grey A Laser Workshop on Laser Based Manufacturing

MR-DMD System Control Room Robotic Work Area Paradigm change Mobility Process goes for the Part to be serviced. Container: DMD Package, Robot, Laser, HVAC, Utilities, etc. Adaptable Integration Different robot size Different laser power (1.-5. kw fiber-coupled Diode Laser, etc.) Cost-effectiveness Robot vs. CNC based platform A Laser Workshop on Laser Based Manufacturing Work Stations

MR-DMD System Robot Fiber optic Conduit Rotary Table Work Table Base Plate A Laser Workshop on Laser Based Manufacturing

Challenges to achieve the vision Remote Manufacturing with hot editing Precision for Near Net shape 3-D components in order of microns Approach: Closed loop Process control to keep outcome to the desired level A Laser Workshop on Laser Based Manufacturing

Overview DMD Process Overview 1. Direct Metal Deposition High power laser builds parts layer-by-layer out of gas atomized metal powder 2. DMD Characteristics.5 dimensional accuracy Fully dense metal Controllable microstructure Heterogeneous material fabrication capability Control over internal geometry A Laser Workshop on Laser Based Manufacturing

Overview DMD Process Overview Water Cooling Laser Beam Channel Power Delivery Channel Blending of 5 common methodologies: Laser CAD CAM Sensors Power Metallurgy Omni directional concentric laser-powder-gas nozzle Shaping Gas Changeable Tip A Laser Workshop on Laser Based Manufacturing

Closed-Loop Process DMD Process Overview CO 2 Laser Closed-loop process Improves dimensional accuracy CAD/CAM Work Table Control Panel NC Chiller Feed-back Controller Power Supply Unit No need for intermediary machining of parts when deposit builds irregularly Near net shape within fraction of millimeter is possible Resulting in significantly reduced post DMD finishing and reduced cost Better thermal control and thus better microstructure control Better microstructure leading to better mechanical properties Significantly reduced distortion and thus post process complication and cost Left: DMD with feedback control Right: DMD without feedback control Example of direct metal fabrication with POM s closed loop height controller. Left: A Laser Workshop on Laser Based Manufacturing w/height controller; Right: no height controller.

Moving Optics DMD Process Overview Why moving optics? Part mass does not affect the usable work envelop (Velocity, acceleration etc) Part handling concerns reduced Angular deposition without moving part 1 Tons Note Complex angles of deposition A Laser Workshop on Laser Based Manufacturing

Energy, Environment, Economy DMD Process Overview Will save energy Will provide designed functionality Will reduce lead time & Economy friendly Environmentally Benign A Laser Workshop on Laser Based Manufacturing

How does DMD Machine Looks Like? DMD Process Overview Why moving optics? Part mass does not the usable work envelop (Velocity, acceleration etc) Part handling concerns reduced Angular deposition without moving part 1 Tons Note Complex angles of deposition A Laser Workshop on Laser Based Manufacturing

DMD System CO 2 Laser Work Table Control Panel NC Chiller Power Supply Unit CAD/CAM Feed-back Controller A Laser Workshop on Laser Based Manufacturing

Comparison of Material Properties: DMD vs. Wrought/Casting Material Fe and steel Material condition Tensile Strength Yield Strength Elongation Elastic Modulus Charpy Impact Hardness (Mpa) (ksi) (Mpa) (ksi) (%) (Gpa) (Mpsi) (J) (ft-lb) (HRC) H 13 H 13, DMD 1643 238 147 24 8.4 197 29 12.9 9.5 54 Wrought H 13 H 13 Wrought (Matweb) 199 289 165 239 9. 27 3 13.6 1. 53 316L SS 316SS, DMD 678 98 515 75 43. 177 26 178. 131.3 23 316L SS wrought Ni-Alloys Wasp Alloy Wrought Wasp alloy Co-Alloys Stellite 21 Cast Stellite 21 316SS, wrought Wasp Alloy, DMD Wasp alloy, wrought aged Stellite 21, DMD Stellite 21, cast 585 85 38 55 45. 193 28 13. 76. 2 948 137 683 99 28. 189 27 127. 93.7 1276 185 897 13 23. 146 21 122 174 972 141 7. 217 31 5.9 4.3 44 62 9 441 64 9. 27 3 21.2 15.6 35 Ti-Alloys Ti6Al4V (Grade V) Wrought Ti6Al4V (V) Al-alloys 447 Al 413 Al (cast) Cu-Alloys Ti6Al4V DMD, Inert atm Ti-6Al4V (V), wrought annealed parallel to deposition 1141 165 145 152 8. 112 16 53.7 39.6 38 95 138 88 128 14. 114 17 17. 12.5 36 288 42 16 23 5.2 74 11 8 HV 241 35 11 16 3.5 71 1 Cu-3 Ni 317 46 24 35 13.9 126 19 12 HV Cu-3 Ni 375 54 234 34 31.5 165 24 128 HV A Laser Workshop on Laser Based Manufacturing

Z (mm) DMD System Overview Conceptualization CAM tool path CAD Data Z Y X Product Y (mm) COMP: 1 2 3 4 5 1 1 m/s.5 1 2 X (mm) 3 -.1 -.2 -.3 DMD with Advanced Modeling, Sensing and Control

Direct Metal Deposition DMD with closed loop control DMD Machine

Mathematical Modeling Process modeling of DMD to develop quantitative relationships between parameters for improved process control

Modeling: Governing Equation Continuity equation: Momentum equation: t u t u uu u l K u l p x Energy equation: C pt t ( c) t Convection term Diffusion term Darcy term u C T k T pl Convection term Conduction term f L f C pt Solute equation: uc D c D c c f c c t l s s t Phase change term at S/L interface s l s u Phase diffusion term Phase motion term

Multiple Track Deposition Model Beam size Transition Finish Overlap Scanning width Start Z The computation domain is not symmetric along laser moving direction Y

Evolution of Temperature Field Laser power: 19 W, beam diameter: 1.8 mm, scanning speed: 6 mm/min, and powder flow rate: 8 g/min.

Z (mm) Z (mm) Composition and Liquid Velocity Distribution Computed chromium concentration profile: Y X COMP: 1 2 3 4 5 y-z surface -.1 COMP: 1 2 3 4 5 -.2.6 1 Y (mm).5 1 m/s 1 2 X (mm) 3 -.3.4.2 x-z surface and x-y surface 1 m/s -.2 3 1 2 Y (mm).5 1 X (mm)

Thermal Cycle

Geometry Control Camera 1 2 3 Image acquisition cards DMD Processing Center (Logic OR) Over limit Laser beam gating signal Height Controller Figure 8 Cladding

Laser power (Kw) Melt pool temperature ( C) Temperature Control: Dynamics Experimental Setup Input and Output GPC Temperature Controller Laser powder 2 15 1 1 2 3 4 Pyrometer Collecting lens Substrate bead 1.2 1.8.6.4 1 2 3 4 Time (s) H13 powder flow rate: 1g/min; Scanning speed: 65mm/min; Standoff: 2mm (beam size 2mm)

Molten pool temperature ( C) (mean of temperature has been removed) Amplitude Temperature (1a) Control: Dynamics State Space Model Step Response: k 1 k k e k X AX Bu K k k k e k y CX Du Dynamic Model Output 4 2 45 4 35 3 25 2 15 1 5 Step Response From: u1 To: y1 System: Goe I/O: u1 to y1 System: Goe Settling Time (sec):.323 I/O: u1 to y1 Rise Time (sec):.165-2 -4.5.1.15.2.25.3.35.4.45 Time (sec) Rise time : 165ms -6 3 4 5 6 7 Time (s)

GPC Controller with constraints Simulink Model 1. GPC Controller 2. A/D D/A interface to DMD process 3. State Estimation

Laser driven voltage (V) Temperature ( Molten pool temperature ( C) Noise and disturbance ( Laser power (W) Simulation: Weight on control: 1 Prediction horizon: 3 Control horizon: 5 Tfilter = [1 -.8] Melt Pool Temperature Control Experimental: Weight on control: 2 1 5 Prediction horizon: 16 Control horizon: 5 Tfilter = [1 -.8] 4 C) 2 22 2-2 -4 5 1 15 2 25.5 18 16 14 5 1 15 2 25 3 35 4 45 1.5 -.5 5 1 15 2 25 1 C) 15 1 5-5 -1 5 1 15 2 25 Time (s).5 5 1 15 2 25 3 35 4 45 time (s) Red: reference temperature Black: experimental

Cladding height (mm) One Inch Cube Cladding with Temperature Control Molten Pool Temperature Control Cladding (a) (b) a 3mm step Substrate b A z y x (c) (d) 1 8 6 With control, a With control, b No control, a No control, b 4 2 Pictures of the deposition at (a) 1 th layer, (b) 2 th layer, (c) 3 th layer and (d) 4 th layer 1 2 3 4 Cladding layer number Cladding height at different layers

Composition Prediction Alloys without Phase Transformation Cr-Fe Ni-Fe Alloys With Phase Transformation Ti-Fe Ni-Al Ni-Ti Hopper1 Hopper2 bead Laser beam Substrate Signal processing unit spectrometer Collecting lens Experimental Setup

Cr-I 434.451nm/Fe-I 43.791nm Cr-I 434.451nm/Fe-I 432.5761nm plasam temperature (K) Cr-I 428.972nm/Fe-I 43.791nm Cr-I 428.972nm/Fe-I 432.5761nm Composition Prediction: Cr-Fe Alloy Calibration Curve 1 1 45.8.9.8 4.6.7.6 395.4 1 2 3 4.5 1 2 3 4 39.7.7 385.6.5.6.5 38.4.4 375.3.3.2 1 2 3 4 Cr weight percentage.2 1 2 3 4 Cr weight percentage 37 5 1 15 2 25 3 35 4 Cr weight percentage Line Intensity Plasma Temperature

composition variation (%) Prediction of Cr% in the Alloy Composition Variation < 5% 1 8 6 4 from single line ratio from temperature from electron density from four averaged line ratio from seven averaged line ratio from seven averaged line ratio and electron density 2-2 -4 5 1 15 2 25 3 35 4 Cr weight ratio percentage

Cr composition (%) Cr composition (%) Composition Sensor for Time Domain Prediction Cr % for 19.8% Cr % for 22.36% 24 22 2 18 16 14 1 2 3 4 5 6 Measurement point 32 3 28 26 24 22 2 18 2 4 6 8 1 12 14 16 18 Measurement point Resolution = 2 δ = 4.1%

Ni 349nm / Fe 363nm Ni 349nm / Fe 364nm Ni 346nm / Fe 363nm Ni 346nm / Fe 364nm Calibration Curve for Fe-Ni Alloy 1-1 1-1 1-1 1 Ni/Fe weight ratio 1-1 1 Ni/Fe weight ratio 1-1 1-1 1-1 1 Ni/Fe weight ratio 1-1 1 Ni/Fe weight ratio

Fe-I 44.6nm/Ti-II 417.4nm Fe-I 47.2nm/Ti-II 417.4nm Fe-I 44.6nm/Ti-II 416.4nm Fe-I 47.2nm/Ti-II 416.4nm Microstructure Sensor: Ti-Fe Alloy (Patent Pending) Calibration Curve 2 1.5 1.5 3 2 1 1 um 55 6 65 7 Weight percent Ti 55 6 65 7 Weight percent Ti 2 3 1.5 1 2.5 2 1.5 Hypereutectic Ti 56 Fe 44.5 55 6 65 7 Weight percent Ti 1 55 6 65 7 Weight percent Ti Hypoeutectic Ti 7 Fe 3 Hypereutectic Ti 62 Fe 38 Eutectic Ti 67.5 Fe 25.5 Hypoeutectic Ti 73 Fe 27

plasma temperature (K) plasma electron density (/cm 3 ) Ti-Fe Alloy Calibration Curve for Ti-Fe Alloy Plasma Temperature Electron Density 62 3 x 116 6 58 56 54 2.5 2 1.5 52 5 48 46 55 6 65 7 75 Weight percent Ti 1.5 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 Cr/Fe weight ratio

Al-I 396.15nm/Ni-I 349.296nm Al-I 396.15nm/Ni-I 352.454nm Al-I 394.4nm/Ni-I 349.296nm Al-I 394.4nm/Ni-I 352.454nm Ni-Al Alloy Phase Transformation and Line Intensity Ratio 8 6 4 3 1um 4 2 2 1 6 7 8 9 Atomic percent Ni 6 7 8 9 Atomic percent Ni 1 5 8 4 6 3 B2 Ni 65 Al 35 4 2 Gamma Prime Ni 8 Al 2 2 1 6 7 8 9 Atomic percent Ni 6 7 8 9 Atomic percent Ni B2 Ni 65 Al 35 Gamma Prime Ni 65 Al 35? Ti 67.5 Fe 25.5

Intensity(Counts) XRD Pattern of Ni8Al2 Sample as Deposited [Z2639.raw] NI8AL2 (111) 3-65-3245> AlNi 3 - Aluminum Nickel 75 5 (2) 25 (1) (11) (21) (211) (3) (22) (311) (222) (4) (32) (321) 2 3 4 5 6 7 8 9 1 11 12 Two-Theta (deg)

Tl-I 417.47nm/Ni-I 344.626nm Tl-I 417.47nm/Ni-I 346.165nm Tl-I 416.414nm/Ni-I 344.626nm Tl-I 416.414nm/Ni-I 346.165nm Ni-Ti Alloy Phase Transformation and Line Intensity Ratio Ni-Ti Alloy.3.4.25.2.3.15.2 Ni 79 TI 21.1.5.2 8 85 9 Atomic percent Ni.1.25 8 85 9 Atomic percent Ni Ni 87 Tl 13.15.2.1.15.1.5.5 8 85 9 Atomic percent Ni 8 85 9 Atomic percent Ni Ni 84 TI 16 1 um Ni 9 Tl 1

Process Model Summary and Conclusion Simulate melt pool temperature, velocity, fluid interface thermal cycle, and composition evolution and distribution Process Sensor and Control Design, Optimization and Implementation Geometry Control Melt pool temperature dynamics and control Composition sensor Microstructure sensor First time in the world one will have the capability to predict the microstructure during the process from plasma, leading to considerable cost and lead time saving

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