the role of powder properties on precision additive metal
TRANSCRIPT
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The role of powder properties on Precision Additive Metal Manufacturing
Mirko Sinico
PAM2 | KU Leuven AM Research Group
Belgium
20th September 2019, Padova, Italy
INFN AM Workshop 2019
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Mirko Sinico – [email protected]
The PAM2 project (https://pam2.eu/)
Slide 1
• Precision Additive Metal Manufacturing
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Mirko Sinico – [email protected]
The PAM2 project (https://pam2.eu/)
Slide 1
• Precision Additive Metal Manufacturing
➢ with 6 Academic partners
➢ and 6 Industrial partners
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Mirko Sinico – [email protected]
The PAM2 project (https://pam2.eu/)
Slide 1
• Precision Additive Metal Manufacturing
➢ with 6 Academic partners
➢ and 6 Industrial partners
• In the domain of Design for AM
• we develop new practices and models
• to enhance the precision of LPBF
• and…we test them on end-users cases
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Mirko Sinico – [email protected]
The PAM2 project (https://pam2.eu/)
Slide 1
• Precision Additive Metal Manufacturing
➢ with 6 Academic partners
➢ and 6 Industrial partners
➢ & 15 young researchers
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Mirko Sinico – [email protected]
The PAM2 project (https://pam2.eu/)
Slide 1
• Precision Additive Metal Manufacturing
➢ with 6 Academic partners
➢ and 6 Industrial partners
• Our goal is to improve the precision of
the LPBF process, covering all the value
chain of the AM manufacturing
• and…test our research on end-users cases
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Mirko Sinico – [email protected]
The role of powder properties on PAM2
Slide 2
precisionprecisionprecision
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Mirko Sinico – [email protected]
Powder properties influence LPBF part properties
Slide 3
from S. Vock, B. Klöden, A. Kirchner, T. Weißgärber, and B. Kieback, ‘Powders for powder bed fusion: a review’, Progress in
Additive Manufacturing, Feb. 2019.
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Mirko Sinico – [email protected]
The role of powder properties on PAM2
Slide 2
Simulations Characterization Processability
Novel full physical meso-scale numerical model
Characterization of AM Metal Powder with an Industrial Microfocus CT
Influence of the Particle Size Distribution on surface quality
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Mirko Sinico – [email protected]
The role of powder properties on PAM2
Slide 4
Simulations
Novel full physical meso-scale numerical model
Mohamad
Bayat
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Mirko Sinico – [email protected]
Novel full physical meso-scale numerical model
Slide 5
Novel full physical meso-scale model:
• Analyze thermal fields, cooling rates,
formation of voids, surface porosities,
surface roughness…
• Taking into account melting/solidification,
evaporation, keyhole formation, radiation,
ray-particle interactions, particle
distribution, powder deposition….
• BUT: e.g. Maraging 300 no surface tension
and viscosity parameters
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Mirko Sinico – [email protected]
The role of powder properties on PAM2
Slide 6
Characterization
Characterization of AM Metal Powder with an Industrial Microfocus CT
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➢ Pushing to the limits our µ-CT Nikon XT H 225 ST by measuring metal powders from 10 µm of Ø
➢ The metrological traceability is maintained by referencing our measurements to Laser Diffraction
analyses (both dry and wet) compliant with ISO 13320-1
Powder particles
Cylindrical
double tape
holder
Specimens preparation µ-CT scan Surface determination MATLAB analysis
• Minimum specimen size to reach high mag.
• Dispersion via dry spraying to avoid
particles in contact
• Powders from Al to W analyzed (various ρ)
• High mag. (69)
• Low power (< 7 W)
• Voxel rescaling via
calibration artifact
• Global ISO 50% thres.
• Our developed local thres.
for comparison
• In-house developed code
• Multiple outputs for particle size distribution
• Multiple outputs for particle shape analysis
Characterization of metal AM powders with µ-CT
Mirko Sinico – [email protected] 9
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Powder particles
Cylindrical
double tape
holder
Specimens preparation µ-CT scan Surface determination MATLAB analysis
• Minimum specimen size to reach high mag.
• Dispersion via dry spraying to avoid
particles in contact
• Powders from Al to W analyzed (various ρ)
• High mag. (69)
• Low power (< 7 W)
• Voxel rescaling via
calibration artifact
• Global ISO 50% thres.
• Our developed local thres.
for comparison
• In-house developed code
• Multiple outputs for particle size distribution
• Multiple outputs for particle shape analysis
Characterization of metal AM powders with µ-CT
➢ Powder distribution, powder shape,
powder porosity, and powder contamination
can be analyzed
PO
RO
SIT
Y
SH
AP
E
Mirko Sinico – [email protected] 9
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Mirko Sinico – [email protected]
The role of powder properties on PAM2
Slide 10
Processability
Influence of the Particle Size Distribution on surface quality
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Mirko Sinico – [email protected]
Starting backwards: an industrial user-case
Slide 11
M. Sinico, R. Ranjan, M. Moshiri, C. Ayas, M. Langelaar, A. Witvrouw, F. van Keulen, and W. Dewulf, A mold insert case study on Topology Optimized design for Additive Manufacturing, Proceedings of the 2019 Annual International Solid Freeform Fabrication Symposium, 2019.
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Mirko Sinico – [email protected]
Industrial user-case requirements
Slide 12
Mold top surface by SPI standard
SPI, Society of Plastic Industry
• From A-3, normal glossy finish,
0.10 µm Ra down to 0.05 µm Ra
• To A-1, super high glossy finish,
0.025 µm Ra down to 0.012 µm Ra
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Mirko Sinico – [email protected]
Typical surface roughness of metal AM
Slide 13
Where we typically are (LPBF)
Our target (glossy finish)
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Mirko Sinico – [email protected]
Industrial user-case requirements
Slide 12
Mold top surface by SPI standard
SPI, Society of Plastic Industry
• From A-3, normal glossy finish,
0.10 µm Ra down to 0.05 µm Ra
• To A-1, super high glossy finish,
0.025 µm Ra down to 0.012 µm Ra
• Several post-processing operations:
rough-milling, grinding, semi-finishing
plus finishing and a final EDM/polishing
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Mirko Sinico – [email protected]
Industrial user-case requirements
Slide 12
Mold top surface by SPI standard
SPI, Society of Plastic Industry
• From A-3, normal glossy finish,
0.10 µm Ra down to 0.05 µm Ra
• To A-1, super high glossy finish,
0.025 µm Ra down to 0.012 µm Ra
• Several post-processing operations:
rough-milling, grinding, semi-finishing
plus finishing and a final EDM/polishing
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Mirko Sinico – [email protected]
Powder properties influence LPBF part properties
Slide 14
If we ↓ decrease average particle size:
• ↑ Increase in purchase cost of the powder (typically)
• ↓ Decrease in flowability
• ↑ Increase in parts surface quality (lower Ra)
surface
quality
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Mirko Sinico – [email protected]
Research methodology
Slide 15
• GOAL:
Test 3 different distributions of Maraging steel 300
• HOW:
Full powder characterization & repeated build job DoE on a ProX 320A machine
Mar 5-15 Mar 10-30 Mar 15-45
DoE at 3 different Ev
𝑬𝒗 =𝑷
𝒗 × 𝒉 × 𝒕
~ 50 J/mm3
~ 60 J/mm3
~ 70 J/mm3
Standard
Array of specimens
~ 20x10x10 mm
t = 30 µm, h = 70 µm
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Mirko Sinico – [email protected]
Powder characterization
Slide 16
Laser Diffraction + Optical microscope
𝑪 =𝟒𝝅𝑨
𝑷𝟐
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Mirko Sinico – [email protected]
Powder characterization
Slide 16
Laser Diffraction + Optical microscope + Industrial µ-CT
𝑺 =𝝅𝟏𝟑(𝟔𝑽)
𝟐𝟑
𝑨
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Mirko Sinico – [email protected]
Powder characterization
Slide 16
+ ASTM B213, B212 and B527 testing
➢ For flowability (Hall Flow),
apparent density ρapp and tap density ρtap
>> 1.25
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Mirko Sinico – [email protected]
Top surface roughness
Slide 17
• Mar 15-45, optimum parameter set
Ra = 12.12 µm
• Optimum at 170 W, ~70 J/mm3 Ev
(1150 mm/s scan speed)
> 99.7 relative density
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Mirko Sinico – [email protected]
Top surface roughness
Slide 17
• Mar 15-45, optimum parameter set
Ra = 12.12 µm
• Mar 10-30, optimum parameter set
Ra = 5.34 µm, 56 % reduction
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Top surface roughness
Slide 17
• Mar 15-45, optimum parameter set
Ra = 12.12 µm
• Mar 10-30, optimum parameter set
Ra = 5.34 µm, 56 % reduction
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Top surface roughness
Slide 17 Mirko Sinico – [email protected]
• Surface roughness is inversely
proportional to Ev, directly
proportional to the laser power, at
the same Ev
• On average, ~40 % Ra reduction
with Mar 10-30
• Stability zone seems wider for the
Mar 10-30 distribution
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Mirko Sinico – [email protected]
Typical surface roughness of metal AM
Slide 13
Our target (glossy finish)
5.34
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(extra) Novel remelting strategy
Slide 18
5.34 µm
• Exploit fine powder possibilities
combined with remelting
Mirko Sinico – [email protected]
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(extra) Novel remelting strategy
Slide 18
5.34 µm
Base at
optimum
parameter set,
t = 30 µm
Top at t = 10 µm
Remelting step
• Exploit fine powder possibilities
combined with remelting
Mirko Sinico – [email protected]
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(extra) Novel remelting strategy
Slide 18
5.34 µm
Base at
optimum
parameter set,
t = 30 µm
Remelting step
• Exploit fine powder possibilities
combined with remelting
Mirko Sinico – [email protected]
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(extra) Novel remelting strategy
Slide 18
5.34 µm
Base at
optimum
parameter set,
t = 30 µm
Top at t = 10 µm
• Exploit fine powder possibilities
combined with remelting
Mirko Sinico – [email protected]
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(extra) Novel remelting strategy
Slide 18
5.34 µm
Base at
optimum
parameter set,
t = 30 µm
Top at t = 10 µm
Remelting step
• Exploit fine powder possibilities
combined with remelting
1.5 µm
Mirko Sinico – [email protected]
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Mirko Sinico – [email protected]
Future steps: acquisition of surface topographies
Slide 19
• Through a Sensofar S neox 3D surface profiler
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Mirko Sinico – [email protected]
Future steps: acquisition of surface topographies
Slide 19
Sa = 12.54 µm Sa = 5.31 µm Sa = 1.98 µm
• Through a Sensofar S neox 3D surface profiler
• Step 1: Acquisition (CLSM) and F-operator (form removal, plane)
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Mirko Sinico – [email protected]
Future steps: acquisition of surface topographies
Slide 19
• Through a Sensofar S neox 3D surface profiler
• Step 1: Acquisition (CLSM) and F-operator (form removal, plane)
• Step 2: S-filter (8 µm) and L-filter (140 µm) to highlight scanning tracks
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Mirko Sinico – [email protected]
Future steps: acquisition of surface topographies
Slide 19
• Through a Sensofar S neox 3D surface profiler
• Step 1: Acquisition (CLSM) and F-operator (form removal, plane)
• Step 2: S-filter (8 µm) and L-filter (140 µm) to highlight scanning tracks
• Step 3: Rescaling to the same Z range
7 µm
-7 µm
7 µm
-7 µm
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Mirko Sinico – [email protected]
The role of powder properties on PAM2
Slide 2
Simulations Characterization Processability
Novel full physical meso-scale numerical model
Characterization of AM Metal Powder with an Industrial Microfocus CT
Influence of the Particle Size Distribution on surface quality
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Thank you!
Mirko Sinico1,2, Wim Dewulf1, and Ann Witvrouw1,2
1 Department of Mechanical Engineering, KU Leuven, 3001 Leuven, BE2 Member of Flanders Make - Core lab PMA-P, KU Leuven, 3001 Leuven, BE
Any question?
Mirko Sinico – [email protected]
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Mirko Sinico – [email protected]
A digression in the decrease of flowability
+
from A. B. Spierings, M.Voegtlin, T. Bauer, and K.Wegener, ‘Powder flowabilitycharacterisation methodologyfor powder-bed-based metaladditive manufacturing’, ProgAddit Manuf, vol. 1, no. 1–2, pp.9–20, Jun. 2016.
Powder with
particle sizes < 5 µm
μ-PBF, modified SLM-50
machine from Realizer
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Mirko Sinico – [email protected]
Future steps
Slide 12
• Complete powders characterization (SEM, rotatory drum flowability test)
• Establish the evolution of surface quality for angled surfaces, and downfacing surfaces
Example for vertical surfaces (90º)
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Mirko Sinico – [email protected]
Future steps
Slide 12
• Complete powders characterization (SEM, rotatory drum flowability test)
• Establish the evolution of surface quality for angled surfaces, and downfacing surfaces
• Establish melt pool variability through the analysis of cross-section micrographs
• Understand the development of surface texture through full 3D topographic measurements
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Mirko Sinico – [email protected]
Conclusions
Slide 11
• Three Maraging 300 powders with different PSDs where tested
• The Mar 5-15 was deemed unsuitable for the ProX 320A recoating system
• A decrease > 50 % of top surface Ra was obtained for the Mar 10-30 distribution
• A novel remelting strategy was developed, resulting in top surface Ra of 1.5 µm
Mar 5-15 Mar 10-30 Mar 15-45
Standard
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(extra) Novel remelting strategy
+
5.34 µm