in situ monitoring , measurement and control of direct digital additive manufacturing

Center for Laser Aided Intelligent Manufacturing University of Michigan, Ann Arbor

In Situ Monitoring , Measurement and control of Direct Digital

Additive Manufacturing

Jyoti Mazumder*

University of Michigan

January 9th, 2013

*Robert H Lurie Professor of Engineering @ University of Michigan


Outline

• Background History of DMD • Introduction

– DMD System Overview• Advances in DMD System

– Geometry Control– Temperature Control– Composition Prediction– Microstructure Prediction– Modeling

• Summary


Vision: Part on Order Anywhere


Running to Moon: Mold & Mirrors

0.5 mm wall thickness in

steel

0.5 mm wall thickness in

steel

Polished to 40 Angstroms!

Polished to 40 Angstroms!


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, wrought

585 85 380 55 45.0 193 28 103.0 76.0 20

Wasp Alloy

Wasp Alloy, DMD

948 137 683 99 28.0 189 27 127.0 93.7

Wrought Wasp alloy

Wasp alloy, wrought aged

1276 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, cast

620 90 441 64 9.0 207 30 21.2 15.6 35

Ti6Al4V (Grade V)

Ti6Al4V DMD, Inert atm

1141 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 deposition

288 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 condition

MaterialTensile Strength Yield Strength Elastic Modulus Charpy Impact

Comparison of Material Properties: DMD vs. Wrought/Casting


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 GrowthTi~ Bright WhiteBone ~ Blue Grey


OIL & GAS SURFACE PROTECTION

AEROSPACEREMANUFACTURING

MEDICALFABRICATION

DEFENSERESTORATION

AUTOMOTIVEPRODUCT ENHANCEMENT

Applications in Industry

AEROSPACEMANUFACTURING

Actual part


DARPA SBIR : Spatial Control of Crystal Texture

Directional growth of grains from bottom to top of the blade

8


9


Challenges to achieve the vision

• Remote Manufacturing with hot editing• Precision for Near Net shape 3-D

components in order of microns• Certify as you build• Approach: In situ monitoring and

Closed loop Process control to keep outcome to the desired level


Overview DMD Process Overview

1. Direct Metal Deposition

• High power (any wave length) laser(or EB[needs Vacuum} ,arc) 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


How do we certify the product during manufacturing or Remanufacturing by in situ monitoring?


DMD System

High Power Laser

CAD/CAM

NC

Feed-backController

WorkTable

ControlPanel

ChillerPowerSupplyUnit


SOMS Solution

14

00.5

1

0

0.5

1-1

-0.5

0

0.5

1

1.5

xy

z

Heat source (Laser / Arc)

Good weld

Porosity

Burn through

Bead separation

Technology

Laser material processing

Plasma spectrum analysis

SMOS

Weld quality, defect type and cause prediction

Features: Real-time Compact size, light weight Low price and low operating cost

Benefits Defect categorization In-situ monitoring of composition

and phase transformation Improved weld quality Reduction in cycle time Reduction of manual inspection Low scrap cost Data collection for post

processing


Defect Detection and Classification

15

0 2000 4000 6000 8000 10000 12000 140000

2000

4000

6000

8000

10000

12000

14000

16000

18000

Wavelength [nm]

Inte

nsit

y [

au

]

• Support vector machine Statistical analysis

Center for Laser Aided Intelligent Manufacturing University of Michigan, Ann Arbor 16

Pin-hole and Porosity

Pin-hole: Small holes located in the surface of the welded seam

- Difficulty of appearance processing & weakness of joining strength

Spatters: loss of the molten pool because of high velocity of liquid

Key factor: Contour of the spectral intensity (e.g. Zn emission line width)


Closed Loop 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

[US patent # 6,122,564 and 6,925,346


Temperature Control: Dynamics

Substrate

bead

powder

Collecting lens

Pyrometer

GPC Temperature Controller

Laser

0 10 20 30 40

1000

1500

2000

Mel

t po

ol t

empe

ratu

re (

0 C)

0 10 20 30 400.4

0.6

0.8

1

1.2

Time (s)

Lase

r po

wer

(K

w)

Experimental Setup Input and Output

H13 powder flow rate: 10g/min; Scanning speed: 650mm/min; Standoff: 20mm (beam size 2mm)


Melt Pool Temperature Control

5 10 15 20 25 30 35 40 451400

1600

1800

2000

2200

Mol

ten

pool

tem

pera

ture

(0 C

)

5 10 15 20 25 30 35 40 450

0.5

1

1.5

time (s)La

ser

driv

en v

olta

ge (

V)

Red: reference temperatureBlack: 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

rpow

er (W

)

0 5 10 15 20 25-100

-50

0

50

100

150

Time (s)

Nois

e a

nd

dis

turb

ance

(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, aWith control, bNo control, aNo control, b

baSubstrate

Cladding

y

zA

x3mm step

One Inch Cube Cladding with Temperature Control


Composition Prediction

Substrate

Collecting lens bead

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 42

8.97

2nm

/Fe-

I 43

0.79

01nm

0 10 20 30 400.5

0.6

0.7

0.8

0.9

1

Cr-

I 42

8.97

2nm

/Fe-

I 43

2.57

61nm

0 10 20 30 400.2

0.3

0.4

0.5

0.6

0.7

Cr-

I 43

4.45

1nm

/Fe-

I 43

0.79

01nm

Cr weight percentage0 10 20 30 40

0.2

0.3

0.4

0.5

0.6

0.7

Cr-

I 43

4.45

1nm

/Fe-

I 43

2.57

61nm

Cr weight percentage5 10 15 20 25 30 35 40

3700

3750

3800

3850

3900

3950

4000

4050

plas

am t

empe

ratu

re (

K)

Cr weight percentage

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 ratiofrom temperature

from electron density

from four averaged line ratio

from seven averaged line ratiofrom seven averaged line ratio and electron density


Ni-Al Alloy Phase Transformation and Line Intensity Ratio (Patent Pending)

60 70 80 900

20

40

60

80

Al-I

394

.4nm

/Ni-I

349

.296

nm

Atomic percent Ni60 70 80 90

0

10

20

30

40

Al-I

394

.4nm

/Ni-I

352

.454

nm

Atomic percent Ni

60 70 80 900

20

40

60

80

100

Al-I

396

.15n

m/N

i-I 3

49.2

96nm

Atomic percent Ni60 70 80 90

0

10

20

30

40

50

Al-I

396

.15n

m/N

i-I 3

52.4

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

nsi

ty(C

ou

nts

)

[Z02639.raw] NI80AL20

03-065-3245> AlNi 3 - Aluminum Nickel

(110)

(210)(211) (300)

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


Mathematical Modeling

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


0

ut

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


Heat transfer / Fluid flowFlow chart & Governing Eqns.

0

l

l

l

l

l

l

l

l s l s

hh k T h h

tu p

u u ut K x

u pv v v

t K y

u pw w w g T T

t K z

cc D c D c c f c c

t

u u

u

u

u

u u

• Governing Equations [1, 2]

Flux due to relative phase motion

Darcy term

Buoyancy term Nov, 2010


Solute Transport: Advection dominant

Pectlet numberC: 3.15x104

Ni:1.69x105

Inside melting pool, Advective transport >> Diffusion transport

Pe Re ScLv

D

Ni concentration C concentration

Nov, 2010

Nominal composition in 4340 steel:1.75% Ni 0.4%


X

Z

YThe computation domain is not symmetric along laser moving direction

Start

Direct

ion o

f tra

vel

in th

e fir

st p

ass

Direct

ion o

f tra

vel

in th

e se

cond p

ass

FinishOverlap

TransitionSca

nning

leng

th

Beam size

Scanning width

Multiple Track Deposition Model


Composition and Liquid Velocity Distribution

X (mm)

01

23Y

(mm

)

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


Thermo-physicalMaterial

Properties

Initialize / Heat Source

Heat, Mass And MomentumCalculation

Is it RapidSolidification?

Non-EquilibriumPartition Coefficient

Calculate the Composition and Phase

No

Is PhaseDetection from

Phase TransformationSensor Same asCalculated One?

No

Product Stop

Yes

Flow Chart

Yes

Change ProcessParameter From

Calculated Co-relations

1

2

3

4

5

6.

7.


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?

in situ monitoring , measurement and control of direct digital additive manufacturing

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