Advances on DMD

Center for Laser Aided Intelligent Manufacturing

University of Michigan, Ann Arbor

A Laser Workshop on “Laser Based Manufacturing”

Advances in Direct Metal Deposition

Jyoti Mazumder* and Lijun Song

University of Michigan

July 15th, 2010

Presented By S H Lee

*Robert H Lurie Professor of Engineering




Outline

• Background History of DMD

• Introduction

– DMD System Overview

• Advances in DMD System

– Modeling

– Geometry Control

– Temperature Control

– Composition Prediction

– Microstructure Prediction

• Summary




Rapid Prototype

Customer

Conceptual Design

Assembly Layouts

Detailed Design

Prototype

Production

Time

Initial CAD Model

SFM

Reduction in

Time and Step




Additive Manufacturing

• Additive Process

• Originally for Rapid Prototyping

Application

• 3-D CAD data -> 2-D Slicing

• Layer by Layer Build-up

• Low Volume 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, ...




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)




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)




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




Part on Order Anywhere




Running to Moon: Mold & Mirrors

0.5 mm wall

thickness in

steel

Polished to 40

Angstroms!




Application In Tissue engineering

5 mm

Titanium scaffold for implantation study in

a mice spinal column

* Image Provided by Prof. Scott Hollister

X-Ray of the Ti-Scaffold After Subcutenous

Bone Growth

Ti~ Bright White

Bone ~ Blue Grey




MR-DMD System

• 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

(1.0-5.0 kW fiber-coupled

Diode Laser, etc.)

– Cost-effectiveness

• Robot vs. CNC based

platform

Work Stations

Control Room Robotic Work Area




MR-DMD System

Work Table

Fiber optic

Rotary Table

Robot

Base Plate

Conduit




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




DMD Process Overview

Copyright 1999 POM Company Inc. All rights reserved




OverviewDMD Process Overview

1. Direct Metal Deposition

• High power laser builds parts layer-by-layer out of gas atomized metal powder

2. DMD Characteristics

• 0.005” dimensional accuracy

• Fully dense metal

• “Controllable” microstructure

• Heterogeneous material fabrication capability

• Control over internal geometry




OverviewDMD Process Overview

Blending of 5 common methodologies:

• Laser

• CAD

• CAM

• Sensors

• Power Metallurgy

Omni directional concentric laser-powder-gas nozzle

Laser Beam Channel

Power Delivery Channel

Water

Cooling

Shaping

Gas

Changeable Tip




Closed-Loop ProcessDMD Process Overview

CO2 Laser

CAD/CAM

NC

Feed-back

Controller

Work

Table

Control

Panel

Chiller

Power

Supply

Unit

Example of direct metal fabrication with POM’s closed loop height controller. Left: w/height controller; Right: no height controller.

Closed-loop process

• Improves dimensional accuracy

• 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




Moving OpticsDMD 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

10 Tons

Note Complex angles

of deposition




Energy, Environment, EconomyDMD Process Overview

• Will save energy

• Will provide designed functionality

• Will reduce lead time & Economy friendly

• Environmentally Benign




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

10 Tons

Note Complex angles

of deposition




DMD System

CO2 Laser

CAD/CAM

NC

Feed-back

Controller

Work

Table

Control

Panel

ChillerPower

Supply

Unit




Elongation Hardness

(Mpa) (ksi) (Mpa) (ksi) (%) (Gpa) (Mpsi) (J) (ft-lb) (HRC)

H 13 H 13, DMD 1643 238 1407 204 8.4 197 29 12.9 9.5 54

Wrought

H 13

H 13 Wrought (Matweb)

1990 289 1650 239 9.0 207 30 13.6 10.0 53

316L SS 316SS, DMD 678 98 515 75 43.0 177 26 178.0 131.3 23

316L SS

wrought

316SS,

wrought585 85 380 55 45.0 193 28 103.0 76.0 20

Wasp

Alloy

Wasp Alloy,

DMD948 137 683 99 28.0 189 27 127.0 93.7

Wrought

Wasp alloy

Wasp alloy,

wrought aged1276 185 897 130 23.0 146 21

Stellite 21Stellite 21,

DMD1202 174 972 141 7.0 217 31 5.9 4.3 44

Cast

Stellite 21

Stellite 21,

cast620 90 441 64 9.0 207 30 21.2 15.6 35

Ti6Al4V

(Grade V)

Ti6Al4V DMD,

Inert atm1141 165 1045 152 8.0 112 16 53.7 39.6 38

Wrought

Ti6Al4V (V)

Ti-6Al4V (V),

wrought

annealed

950 138 880 128 14.0 114 17 17.0 12.5 36

4047 Alparallel to

deposition288 42 160 23 5.2 74 11 80 HV

413 Al

(cast)241 35 110 16 3.5 71 10

Cu-30 Ni 317 46 240 35 13.9 126 19 120 HV

Cu-30 Ni 375 54 234 34 31.5 165 24 128 HV

Al-alloys

Cu-Alloys

Fe and steel

Co-Alloys

Ti-Alloys

Ni-Alloys

Material

conditionMaterial

Tensile Strength Yield Strength Elastic Modulus Charpy Impact

Comparison of Material Properties: DMD vs. Wrought/Casting



DMD System Overview

Conceptualization CAD Data CAM tool path

ProductDMD with Advanced

Modeling, Sensing and

ControlX (mm)

0

1

2

3Y(m

m)

0.5

1

Z(m

m)

-0.3

-0.2

-0.1

0

Y

X

Z

COMP: 1 2 3 4 5

1 m/s



Direct Metal Deposition

DMD with

closed loop controlDMD Machine



Mathematical Modeling

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



0

u

t

x

pu

Kuu

t

u

l

l

u

Continuity equation:

Momentum equation:

Energy equation:

t

TCf

t

fTkTC

t

TC psspl

p

Lu

Solute equation: uu slsl ccfccDcDct

c

)(

Convection term Diffusion term Darcy term

Convection term Conduction term Phase change term at S/L

interface

Phase diffusion term Phase motion term

Modeling: Governing Equation



Z

Y

The computation domain is not symmetric

along laser moving direction

StartFinish

Overlap

TransitionBeam size

Scanning

width

Multiple Track Deposition Model



Evolution of Temperature Field

Laser power: 1900 W, beam diameter: 1.8 mm, scanning speed: 600 mm/min,

and powder flow rate: 8 g/min.



Composition and Liquid Velocity Distribution

X (mm)

0

1

2

3Y(m

m)

0.5

1

Z(m

m)

-0.3

-0.2

-0.1

0

Y

X

Z

COMP: 1 2 3 4 5

1 m/s

X (mm)

0

1

2

3

Y(m

m)

0

0.5

1

Z(m

m)

-0.2

0

0.2

0.4

0.6

COMP: 1 2 3 4 5

1 m/s

Computed chromium concentration profile:

x-z surface and x-y surface

y-z surface



Thermal Cycle



Geometry Control

DMD Processing

Center (Logic OR)

Laser beam gating signal

Camera 1 2 3

Image acquisition

cards

Over limit

Height Controller Figure 8 Cladding



Temperature Control: Dynamics

Substrate

bead

powder

Collecting lens

Pyrometer

GPC Temperature

ControllerLaser

0 10 20 30 40

1000

1500

2000

Melt p

ool te

mpera

ture

(0C

)

0 10 20 30 400.4

0.6

0.8

1

1.2

Time (s)

Laser

pow

er

(Kw

)

Experimental Setup Input and Output

H13 powder flow rate: 10g/min; Scanning speed: 650mm/min;

Standoff: 20mm (beam size 2mm)



Temperature Control: Dynamics

30 40 50 60 70-600

-400

-200

0

200

400

Time (s)

Molten p

ool te

mpera

ture

(0C

)

(mean o

f te

mpera

ture

has b

een r

em

oved)

Step Response

Time (sec)

Am

plit

ude

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.450

50

100

150

200

250

300

350

400

450

System: Goe

I/O: u1 to y1

Settling Time (sec): 0.323

From: u1 To: y1

System: Goe

I/O: u1 to y1

Rise Time (sec): 0.165

Dynamic Model Output

Step Response:

1k k k e k X AX Bu K

k k k e k y CX Du

(1a)

State Space Model

Rise time : 165ms



GPC Controller with constraints – Simulink Model

1. GPC Controller 2. A/D D/A interface to DMD process

3. State Estimation



Melt Pool Temperature Control

5 10 15 20 25 30 35 40 451400

1600

1800

2000

2200

Molten p

ool te

mpera

ture

(0C

)

5 10 15 20 25 30 35 40 450

0.5

1

1.5

time (s)

Laser

driven v

oltage (

V)

Red: reference temperature

Black: experimental

0 5 10 15 20 25-400

-200

0

200

400

Tem

pera

ture

(0C

)

0 5 10 15 20 25-0.5

0

0.5

Lase

rpo

wer

(W)

0 5 10 15 20 25-100

-50

0

50

100

150

Time (s)

Noi

se a

nd

dist

urba

nce

(0C

)

• Experimental:

• Weight on control: 2×105

• Prediction horizon: 16

• Control horizon: 5

• Tfilter = [1 -0.8]

• Simulation:

• Weight on control: 100000000

• Prediction horizon: 30

• Control horizon: 5

• Tfilter = [1 -0.8]



(a) (b)

(c) (d)

Pictures of the deposition at (a) 10th layer,

(b) 20th layer, (c) 30th layer and (d) 40th layer Cladding height at different layers

Molten Pool Temperature Control

0 10 20 30 400

2

4

6

8

10

Cladding layer number

Cla

dd

ing

he

igh

t (m

m)

With control, a

With control, b

No control, a

No control, b

ba

Substrate

Cladding

y

zA

x3mm step

One Inch Cube Cladding with Temperature Control



Composition Prediction

Substrate

Collecting lensbead

Laser beam

Signal processing

unit

spectrometer

Hopper1

Hopper2

Alloys without Phase

Transformation

Cr-Fe

Ni-Fe

Alloys With Phase

Transformation

Ti-Fe

Ni-Al

Ni-Ti

Experimental Setup



Composition Prediction: Cr-Fe Alloy

• Calibration Curve

0 10 20 30 400.4

0.6

0.8

1

Cr-

I 428.9

72nm

/Fe-I

430.7

901nm

0 10 20 30 400.5

0.6

0.7

0.8

0.9

1

Cr-

I 428.9

72nm

/Fe-I

432.5

761nm

0 10 20 30 400.2

0.3

0.4

0.5

0.6

0.7

Cr-

I 434.4

51nm

/Fe-I

430.7

901nm

Cr weight percentage

0 10 20 30 400.2

0.3

0.4

0.5

0.6

0.7

Cr-

I 434.4

51nm

/Fe-I

432.5

761nm


5 10 15 20 25 30 35 403700

3750

3800

3850

3900

3950

4000

4050

pla

sam

tem

pera

ture

(K

)


Line Intensity Plasma Temperature



Prediction of Cr% in the Alloy

• Composition Variation < 5%

5 10 15 20 25 30 35 40-40

-20

0

20

40

60

80

100

Cr weight ratio percentage

com

positio

n v

ariation (

%)

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



Composition Sensor for Time Domain Prediction

• Cr % for 19.80% Cr % for 22.36%

0 10 20 30 40 50 6014

16

18

20

22

24

Cr

com

positio

n (

%)

Measurement point0 20 40 60 80 100 120 140 160 180

18

20

22

24

26

28

30

32

Cr

com

positio

n (

%)

Measurement point

Resolution = 2×δ = 4.1%



Calibration Curve for Fe-Ni Alloy

10-1

100

10-1

Ni 346nm

/ F

e 3

63nm

Ni/Fe weight ratio

10-1

100

10-1

Ni 346nm

/ F

e 3

64nm

Ni/Fe weight ratio

10-1

100

10-1

Ni 349nm

/ F

e 3

63nm

Ni/Fe weight ratio

10-1

100

10-1

Ni 349nm

/ F

e 3

64nm

Ni/Fe weight ratio



55 60 65 700

0.5

1

1.5

2

Fe-I

404.6

nm

/Ti-II

416.4

nm

Weight percent Ti

55 60 65 700

1

2

3

Fe-I

407.2

nm

/Ti-II

416.4

nm

Weight percent Ti

55 60 65 700.5

1

1.5

2

Fe-I

404.6

nm

/Ti-II

417.4

nm

Weight percent Ti

55 60 65 701

1.5

2

2.5

3

Fe-I

407.2

nm

/Ti-II

417.4

nm

Weight percent Ti

Microstructure Sensor: Ti-Fe Alloy (Patent Pending)

• Calibration Curve

Eutectic Ti67.5Fe25.5

Hypereutectic Ti56Fe44

Hypoeutectic Ti73Fe27 Hypereutectic Ti62Fe38

Hypoeutectic Ti70Fe30

10 um



Calibration Curve for Ti-Fe Alloy

• Ti-Fe Alloy

Plasma Temperature Electron Density

55 60 65 70 754600

4800

5000

5200

5400

5600

5800

6000

6200

Weight percent Ti

pla

sm

a t

em

pera

ture

(K

)

1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.80

0.5

1

1.5

2

2.5

3x 10

16

Cr/Fe weight ratiopla

sm

a e

lectr

on d

ensity (

/cm

3)



Ni-Al Alloy Phase Transformation and Line

Intensity Ratio

60 70 80 900

20

40

60

80

Al-I

394.4

nm

/Ni-I

349.2

96nm

Atomic percent Ni

60 70 80 900

10

20

30

40

Al-I

394.4

nm

/Ni-I

352.4

54nm

Atomic percent Ni

60 70 80 900

20

40

60

80

100

Al-I

396.1

5nm

/Ni-I

349.2

96nm

Atomic percent Ni

60 70 80 900

10

20

30

40

50

Al-I

396.1

5nm

/Ni-I

352.4

54nm

Atomic percent Ni

10um

? Ti67.5Fe25.5

B2 Ni65Al35

Gamma Prime Ni65Al35

B2 Ni65Al35

Gamma Prime Ni80Al20



XRD Pattern of Ni80Al20 Sample as Deposited

(111)

(200)

(220) (311)

(100)

20 30 40 50 60 70 80 90 100 110 120

Two-Theta (deg)

0

2500

5000

7500

Inte

nsity(C

ou

nts

)

[Z02639.raw] NI80AL20

03-065-3245> AlNi 3 - Aluminum Nickel

(110)

(210)(211) (300)

(222) (400)(321)(320)



• Ni-Ti Alloy

Ni-Ti Alloy Phase Transformation and Line

Intensity Ratio

80 85 900.05

0.1

0.15

0.2

0.25

0.3

Tl-I

416.4

14nm

/Ni-I

344.6

26nm

Atomic percent Ni

80 85 90

0.1

0.2

0.3

0.4

Tl-I

416.4

14nm

/Ni-I

346.1

65nm

Atomic percent Ni

80 85 900

0.05

0.1

0.15

0.2

Tl-I

417.4

07nm

/Ni-I

344.6

26nm

Atomic percent Ni

80 85 900

0.05

0.1

0.15

0.2

0.25

Tl-I

417.4

07nm

/Ni-I

346.1

65nm

Atomic percent Ni

100 um

Ni90Tl10

Ni87Tl13Ni79TI21

Ni84TI16



Summary and Conclusion

• Process Model

– 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



Thank you for your

attention!

Any questions or

comments?

Advances on DMD

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