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In-Situ Process Monitoring for Series Production of Titanium Parts

Additive World Conference 2018Eindhoven, NV

March 14-15, 2018

Mark J. ColaPresident and CTO

Sigma Labs, Inc.3900 Paseo del SolSanta Fe NM 87507

www.sigmalabsinc.com

This document contains confidential and proprietary information of Sigma Labs, Inc and is protected by patent, copyright, trade secret and other State and Federal laws. Its receipt or possession does not convey any rights to reproduce, disclose its contents, or to manufacture, use or sell anything it may describe. Reproduction, disclosure, or use without specific written authorization of Sigma Labs, Inc is strictly forbidden 1

Outline

• Objectives

• Introduction

• Experimental Approach

• Results

• Case Study

• Summary

• Acknowledgements

This document contains confidential and proprietary information of Sigma Labs, Inc and is protected by patent, copyright, trade secret and other State and Federal laws. Its receipt or possession does not convey any rights to reproduce, disclose its contents, or to manufacture, use or sell anything it may describe. Reproduction, disclosure, or use without specific written authorization of Sigma Labs, Inc is strictly forbidden

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Objectives

Demonstrate the ability of Sigma’s PrintRite3D INSPECT® in-process QA software to:

1. Enable Rapid Feature Based Qualification

2. Create Feature-based Process Space Maps using In-situ Thermal Energy Density™ Data

3. Establish one-to-one correlations between In-situ Thermal Energy Density™ Data and Ex-situ Quality Metric Data


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INTRODUCTION

Effects of Power and Velocity Changes on Energy Density

This document contains confidential and proprietary information of Sigma Labs, Inc and is protected by patent, copyright, trade secret and other State and Federal laws. Its receipt or possession does not convey any rights to reproduce, disclose its contents, or to manufacture, use or sell anything it may describe. Reproduction, disclosure, or use without specific written authorization of Sigma Labs, Inc is strictly forbidden.

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Parametric Effects on Energy DensityProcess Map - Traditional View

P-V Space Map

Suter, R.M., Liu, H., and Rollet, A.D., 2017, “Towards optimal processing of additive manufactured metals for high strain rate properties”, presented at SSSAP, Chicago, IL, April.

Energy Density Contours Optimum Energy Density Contour


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Other Process Effects on Energy DensityProcess Map - Traditional View

Layer Thickness Effects


Preheating Effects


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Process Input Parameter EffectsMelt Pool Geometry

Melt Pool Size Effects

Heigel, J.C., Lane, B.M., 2017, “Measurement of the melt pool length during single scan tracks in a commercial laser powder bed fusion process”, NIST MSEC2017-2942, Proceedings of the

2017 ASME International Conference on Manufacturing Science and Engineering, Los Angeles, CA, June 4-8.

Alloy 625


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Feature Based QualificationMelt Pool is the Building Block Feature and Control Volume for Monitoring

Alloy 625

Yield Improvement

EXPERIMENTAL APPROACH

Power and Velocity Changes


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Specimen Configuration

• Material:

• Titanium Ti-6Al-4V

• Density: 4.43 g/cc

• Machine:

• EOS M290

• Method:

• Build Layout: array

• Input parameter combinations: 32

• Replicates of each condition: 3

• Scan Strategy: 67° rotation

• Layer Thickness: 30μm

• Upskins, Downskins and Contour

parameters: On

• Sensors:

• Non-imaging optical photodetectors: 2

• Non-contact thermal: 1

• X&Y Position: 1

• Data Collection Rate:

• 50,000 samples/sec


Build Settings

Specimen Isometric View

Top Down View of Build Plate

Designed to emphasize scan

conditions, e.g., upskin,

downskin, etc.

25mm

Parametric Variations


SettingSpeed

(mm/s)Power

(W)GED

(J/mm2)

Speed Change

(%)

Power Change

(%)

GED Change

(%)

1 900 210 1.667 -25% -25% 0%

2 900 350 2.778 -25% 25% 67%

3 960 336 2.500 -20% 20% 50%

4 960 224 1.667 20% -20% 0%

5 960 280 2.083 -20% 0% 25%

6 1200 280 1.667 0% 0% 0%

7 1200 336 2.000 0% 20% 20%

8 1200 224 1.333 0% -20% -20%

9 1440 336 1.667 20% 20% 0%

10 1440 280 1.389 20% 0% -17%

11 1440 224 1.111 20% -20% -33%

12 1500 210 1.000 25% -25% -40%

13 1500 350 1.667 25% 25% 0%

• Nominal Parameter Settings: 280 W, 1200 mm/s, 0.14 mm

• Off-nominal Parameter Conditions: percentage from nominal setting

• Build Qty: Two (2)

Build Parameters

SettingSpeed

(mm/s)Power

(W)GED

(J/mm2)

Speed Change

(%)

Power Change

(%)

GED Change

(%)

14 600 168 2.000 -50% -40% 20%

15 600 196 2.333 -50% -30% 40%

16 600 280 3.333 -50% 0% 100%

17 600 364 4.333 -50% 30% 160%

18 840 168 1.429 -30% -40% -14%

19 840 196 1.667 -30% -30% 0%

20 840 280 2.381 -30% 0% 43%

21 840 364 3.095 -30% 30% 86%

22 1200 168 1.000 0% -40% -40%

23 1200 196 1.167 0% -30% -30%

24 1200 364 2.167 0% 30% 30%

25 1560 168 0.769 30% -40% -54%

26 1560 196 0.897 30% -30% -46%

27 1560 280 1.282 30% 0% -23%

28 1560 364 1.667 30% 30% 0%

29 1680 168 0.714 40% -40% -57%

30 1680 196 0.833 40% -30% -50%

31 1680 280 1.190 40% 0% -29%

32 1680 364 1.548 40% 30% -7%


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P-V Space MapTraditional View

P-V Space Map

Maher, M., 2016. “Open Manufacturing Overview”, ARPA-E METALS Annual Meeting, Additive Manufacturing Panel Detroit, MI., August 23.

(https://arpa-e.energy.gov/sites/default/files/documents/files/DARPA_OM%20at%20ARPAe.pdf)

“Make & Break” Qualification is

too Costly

Typical Aerospace Manufacturer

• Material Property Evaluation

• 5,000-100,000 coupons

• $8-15M

• 2 yrs

RESULTS

Effects of Power and Velocity Changes on In-situ Dynamical Melt Pool Behaviors


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PhotographsProcess Characterization Builds

Build 1 Build 2


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In-Situ Thermal Energy Density™ 3D ImageSpatiotemporal Thermal Signatures – Build 1

Contour scans turned off

• Resolution Level:

100μm

• Scaling:

Relative to build itself

Blue = Lowest

Red = Highest

Changes to process input parameters

result in corresponding changes in

Thermal Energy Density™ Value

In-Situ Data Details


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In-Situ Thermal Energy Density™ 3D ImageSpatiotemporal Thermal Signatures – Build 2

Contour scans turned on


100μm

• Scaling:


Blue = Lowest

Red = Highest

Changes to process input parameters result

in corresponding changes in Thermal Energy

Density™ Value



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In-Situ Thermal Energy Density™ 2D ImagesBuilds 1 & 2


100μm

• Scaling:


Blue = Lowest

Red = Highest

Changes to process input

parameters result in corresponding

changes in Thermal Energy

Density™ Value



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Parametric Effects on Energy Density

P-V Space Map


Energy Density Contours Optimum Energy Density Contour

Part density is a key indicator of melting quality - if the part is porous it is unlikely to exhibit the strength, ductility and fatigue / creep performance needed.


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P-V Space MapTraditional View with Ex-Situ Test Results

P-V vs. Ex-Situ Part Density Data

Nominal

P-V Setting


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P-V Space MapTraditional View with Ex-Situ Test Results & Energy Density Contour

P-V vs. Ex-situ Part Density Data

Optimum

Energy Density

Contour?


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P-V Space MapTraditional View with In-Situ Test Results and Energy Density Contour

P-V vs. Sigma’s Thermal Signature In-Situ Data

Optimum

Energy Density

Contour?

P-V Space MapNovel View with Sigma’s Thermal Signatures: In-Situ vs. Input Parameters


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Scanning Velocity (mm/s)

La

se

r P

ow

er

(W

)

In-Situ

(TED™)

P-V vs. In-Situ Data

P-V Space MapNovel View with Sigma’s Thermal Signatures: In-Situ & Ex-Situ vs. Input Parameters


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> 97%

< 97%

La

se

r P

ow

er

(W

)

In-Situ

(TED™)

Ex-Situ

(Density)

P-V vs. In-Situ and Ex-Situ Data


P-V Space MapNovel View with Sigma’s Thermal Signatures: In-Situ & Ex-Situ vs. Input Parameters


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> 97%

< 97%

La

se

r P

ow

er

(W

)

In-Situ

(TED™)

Ex-Situ

(Density)



Conduction Mode

Lack of Fusion

Optimum

Energy Density

Contour

P-V Space MapNovel View with Sigma’s Thermal Signatures, Notional Process Window & Control Envelope


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> 97%

< 97%

La

se

r P

ow

er

(W

)

In-Situ

(TED™)

Ex-Situ

(Density)



Conduction Mode

Lack of Fusion

Optimum

Energy Density

Contour

Control

Envelope

Process

Window

• Process Window:

User defined such

as +/- 3σ

• Control Envelope:

User defined such

as +/- 1σ

Details

La

se

r P

ow

er

(W

)



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P-V Space MapNovel View with In-Situ & Ex-Situ Data and Energy Density Contour

P-V, In-Situ & Ex-situ Data

“Make & Break” Qualification is

too Costly

Typical Aerospace Manufacturer


• 5,000-100,000 coupons

• $8-15M

• 2 yrs

“Sigma’s Software enables

Rapid Feature-based

Qualification


• Reduced No. Coupons

• Reduced $

• Reduced Time

Correlation PlotsIn-Situ Thermal Energy Density™ vs. Global Energy Density & Ex-Situ Part Density: Builds 1 & 2


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Changes to P-V represented here as GED, result in near one-to-one corresponding

change in In-Situ Thermal Energy Density™ metric.

Changes to P-V represented here as in-situ TED™, result in a corresponding

change in part density. Population size is subset of original data set.

CASE STUDY – INITIAL RESULTS

Effects of Population Density on In-Situ Thermal Energy Density™


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High Population Density BuildDemonstrate capability of Sigma’s QA software to identify process consistency and part quality


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• No. of Specimens: 667

• Material: Ti-6Al-4V

• Prototypic Mockup: dental implant

• Process P-V Settings: Mfgr’s Recommendation

High Population Density BuildSpatiotemporal In-Situ Thermal Energy Density™ Images


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Sigma’s Proprietary 3D Thermal Energy Density™ Point Cloud Cross Section

Scan types clearly distinguishable from each other, e.g., outskins; inskins; top skins; and support structure

High Population Density BuildIn-Situ Thermal Energy Density™ Trend Plots


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Part by Part

Layer by Layer

Different TEDavg values for each part reflect within build variations

Different TEDavg values for each layer reflect within part feature variations

Summary

Sigma’s PrintRite3D INSPECT® in-process QA software can:

1. Enable Rapid Feature-Based Qualification

2. Create Feature-based Process Space Maps using In-situ Thermal Energy Density™ Data

3. Establish one-to-one correlations between In-situ Thermal Energy Density™ Data and Ex-situ Quality Metric Data


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Acknowledgements

The authors are grateful for the many technical contributions from Scotts Betts, Michael Brennen, Lars Jacquemetton, Alberto Castro and Darren Beckett of Sigma Labs, and the many useful technical discussions with Dr. R. Bruce Madigan of Montana Tech University.


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Thank You

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