opportunities and challenges in rapid...
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OPPORTUNITIES AND CHALLENGES IN RAPID
FLEXIBLE MANUFACTURING
Jian Cao
Professor of Mechanical Engineering
Director, Northwestern Initiative for Manufacturing Science and Innovation
Associate VP for Research
Northwestern University
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MA
UFA
CT
UR
ING
&
PO
PU
LA
TIO
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Economic history – Share of world GDP
Population history – Share of world population
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MA
UFA
CT
UR
ING
TR
EN
D
TYPE OF MANUFACTURING
DISTRIBUTED
1800 AD
Self-reliance
CONCENTRATED
2000 AD
Reliance on others
DISTRIBUTED ???
2100 AD
Self-reliance ???
CURRENT FORCES NOW AT WORK
• Globalization
• Cyber Infrastructure
• Technological Advances
• Mass Customization / Personalization
• Emergence of Point-of-use Technologies
Rapid Flexible Manufacturing
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Dieless Tri-Pyramid Robots for Rapid Forming D
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Jian Cao, ampl.mech.northwestern.edu
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AD
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amount of white space
een photos and header
Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin
Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. SAND NO. 2011-XXXXP
Predictive Theory and Computational Approaches for Additive Manufacturing
Justine Johannes Engineering Sciences Director , Sandia National Laboratories
Contributors: Mark Smith, Tony Geller, Arthur Brown, Mario Martinez, Joe Bishop, Ben Reedlunn, David Adams,
Jay Carroll, Mike Maguire, Bo Song, Jack Wise, Brad Boyce,
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30+ yrs of Sandia Additive Manufacturing Technology Development & Commercialization
FastCast*
Development
housing
LENS®*
Stainless
housing
LIGA
“Hurricane” spring
MEMS SUMMIT™*
Micro gear assembly
Spray Forming
Rocket nozzle
* Licensed/Commercialized Sandia AM technologies
RoboCast*
Energetic
Materials Ceramic
parts
Sandia Hand
50% AM built
Direct Write
Conformal electronics
Printed battery Current Capability/Activity
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Potential Cost/Schedule/Design/Risk Benefits
Optimize for Performance, Not Machinability Revolutionary new design possibilities Engineering analysis driven designs
Engineered Materials Multi-material and graded material parts Future potential for microstructural control?
GE Additive Manufacturing Design Competition
Ti-6Al-4V
Inconel 718
“We can now control local material properties, which will change
the future of how we engineer metallic components,” R. Dehoff
Drivers for AM for National Security Applications
AM Design 0.7 lb. • 84% wt. reduction
• Successful load tests
Original Design 4.5 lb.
pad side view
Sandia Mass Mock
Easily customize weight, center of gravity, moment of inertia
AM Inconel 718 Crystallographic Orientation
Control Demo’d at ORNL
Sandia LENS® Functionally Graded Materials
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Enabling Design through Computational Simulation
Process → Microstructure → Properties → Performance Relationships
Key Advancements Needed
Improved fundamental understanding of AM process to macroscale properties
Predict response from knowledge and control of microscale process
Constitutive model advancement
Predict response from stochastic process knowledge leading to quantified performance uncertainty
Topology optimization driven by advance computational approached, coupled with in-situ metrology, to modernize design
Particle
packing
Particle
heating
Partial melt &
flow
Topology issues
& surface finish
Microstructure
Molten pool
dynamics
Solidification
10 cm/sec
traveling seam weld
(Sierra, Martinez)
Predictive tools and approaches
opportunity for innovation
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Lens Process Fully dense metal Good mechanical properties Graded materials Add to exiting parts
Exemplar of Materials and Computational Challenges
Part heats up during the build & heat flow changes -- so microstructure
& properties in the top (I), middle (II), & base (III) may differ
Process characterization/modeling
David Keicher (1815), John Smugeresky (8247)
Uni-directional Solidification - Built narrow “wires” to achieve 1-D
heat flow to simplify & understand
solidification front
- Simplified comparison with model
predictions
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AM Materials Are Unlike Conventional Mat’ls
Near Term (traditional approach)
• Property measurements
• Microstructure/defect analyses
• H2 compatibility/permeability
• Statistical variability analyses
• Effects of post treatments
• …
Future State (predictive modeling w QMU)
• Establish microstructure-properties-
performance relationships
• Multi-scale modeling/validation
• Poly-crystal plasticity models
• Direct numerical simulation
• …
Engineered Materials Reliability (EMR) Research Opportunity: Develop a framework for
understanding how material variability impacts the reliability of engineered products through the
use of multi-scale computational and experimental approaches that account for variability across
length scales and provide probabilistic predictions of product performance.
304L
SS
1.0 mm
Wrought Additive
LENS (3.8kW)
Stress field using homogenization theory
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Metrology for AM Is Also A Key Challenge
Family of artifacts designed, 3D printed, & measured Opportunity to develop better AM metrology artifacts
Unique challenges for process/equip. characterization Tolerance/Surface Finish/Properties vary with machine, material,
print orientation, support structures, post-processing,…)
Ti-6Al-4V polyhedron & “Manhattan” artifacts
for MPE (maximum permissible error)
Ti “Manhattan” error map
17-4PH polyhedron texture anisotropy map
Hy Tran (2523), Bradley Jared (1832)
Siemens star geometries for resolution evaluation
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AM Design Via Functionality Prioritization
Analysis-Driven Design Optimization Critical to Advance Fundamental Understanding along with
Computational Approaches
Core of a dead Cholla cactus. It is interesting that optimized designs often resemble natural structures (bio-mimicry).
We combined Topological Optimization (TO) with
eXtended Finite Element Modeling (X-FEM) & LENS® to optimize selected properties, e.g., strength/weight ratio.
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VO
IDS
& IN
CLU
SIO
NS
Wolff et al., ICOMM 2015
ampl.mech.northwestern.edu
• Strain-based voids due to lack of fusion of powders
• Stress-based oxides due to the entrapped gas
• Fracture surfaces reveal coalesced large voids and increased average concentration of oxygen
Strain-based voids between 10 and 200 µm in diameter
Voids (5-20 µm) at fracture
Micro-voids1-5 µm in diameter
Stress-based oxide inclusions ~100 µm in diameter
Stainless Steel 316L (LENS)
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B samples increase in
strength from top to center
layers
C samples increase in
ductility from surface to core
(within the same build
layer)
A samples increase in
strength from surface to core
of block
X
Y
Z Build direction
Scan direction
A
B
C
X
Y
Z
NO
N-U
IFO
RM
ITY
Wolff et al., RAPID 2015
ampl.mech.northwestern.edu
Ti-6Al-4V Mechanical Testing
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HP
C C
HA
LLE
NG
ES
Process time vs. solidification time
Software/parallelization limitations
Controls total
simulation time Controls time
increment
Implicit analysis
requires more
memory
Software
requires more
information
Increases memory
requirement
Poor scaling for
large-scale models
Part size vs. particle cluster size Controls
maximum
element size
Controls total
number of DOFs
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Comparison w/ Abaqus Solution
Quantity of
Interest Abaqus
In-House
Code
Pea
k T
emp
()
Min 1126 1150
Max 1433 1384
Diff 307 234
Avg 1280 1267
Temperature
Resolution (℃) 110 5
Solution Steps 301 5556
Computation
Time (s) 39.0 2.08 R
ES
OLU
TIO
N V
S T
IME
Smith, J., Liu, W.K., and Cao, J.
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Electrospinning
Q
Pump
Buckling
Axisymmetric charged jet
Spinneret
Bending instability
Whipping motion
Collector
High
voltageE
Splitting
+5~50 kV
+200~1000 V
ETaylor cone
(a) (b)
(c) (d)
Schematics illustration of (a) far-field electrospinning (b) Near-field electrowriting processes, which
produce (c) random [adapted from Science, 2004], and (d) aligned nanofibers [Xu et al. 2014, ICOMM]
MO
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PR
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ampl.mech.northwestern.edu
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3D
PR
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Conductive Bioscaffolds
Hersam & Shah
ACS Nano, in press, 2015
High-content (60 vol%) graphene inks can be 3D printed into self-
supporting, electrically conductive, and mechanically resilient structures
(e.g., implantable tubular nerve conduits)
1468: Manufacturing Machines and
Equipment (MME)
ZJ Pei Program Description: o Supports fundamental research to generate knowledge that will be used to either design
new or improve existing manufacturing machines and equipment
o “Manufacturing” is “the process of converting raw materials, components, or parts into
finished goods that meet a customer's expectations or specifications”
o Covers:
• Material removal
• Additive manufacturing
• Energy manufacturing
• Bio/medical manufacturing
Context of MME Supported Research
Machine/Equipment
Inputs(Raw Materials)
Outputs(Products)
• Wind• Sunlight• Carbon dioxide• Biomass• Seawater• Metal• Ceramics• Composite
• Electricity• Biofuel• Drinking water• Aircraft• Medical device• On-demand organ
• Wind turbine• Solar panel• Artificial photosynthesis device• Additive manufacturing machine• Grinder• Drilling machine
Emphasis Areas: o Basic understanding (predictive
models)
o Innovative processes
o Industry relevance
Program in Context
Role of the Program: o Community served: 53% ME, followed by 17%
IE and 6% AE
o Supports fundamental research, not
development, tech transfer, commercialization
o Complementary to other NSF programs
(SBIR/STTR, AIR, IUCRC) and other agencies
(DOD, DOE, NIST)
Partnerships: o MPM, NM, MES, ESD, CS, MoM
o IIP, CBET, ECCS, EEC; CISE, EHR
o DOD, NIST, DOE, …
Important NSF or National
Initiatives: o INSPIRE
o National Network for Manufacturing Innovation (NNMI)
o National Additive Manufacturing Innovation Institute (NAMII)
Chemical Engineering, 0.5%
Petroleum Engineering, 0.5%
Biomedical Engineering, 1.0%
Civil/Environmental Engineering, 1.0%
Mining Engineering, 2.0%
Engineering Management, 2.5%
Engineering Science & Physics, 2.5%
Other, 2.5%Electrical & Computer
Engineering, 3.0%
Biological & Agricultural
Engineering, 4.1%
Materials Engineering, 4.6%
Aerospace Engineering, 5.6%
Industrial Engineering, 17.3%
Mechanical Engineering, 52.8%
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1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Nu
mb
er
of
Pro
po
sals
Year
Prop # CAREER Proposals
Looking Forward
Research Challenges and Opportunities
MME research community lacks of junior faculty (very few CAREER proposals)
U.S. needs more MME academic leaders, PhDs, more high-quality research of industry
relevance
Using various resources to expand MME research community
NSF Workshop on Frontiers of Additive Manufacturing Research and Education, 7/11-12, 2013
NSF Workshop on Future Research Needs in Advanced Manufacturing from Industrial
Perspective, 8/12-13, 2013
Establish IUCRCs in MME areas
Establish National Network for
Manufacturing Innovation (NNMI)
institutes in MME areas
Partnership with other agencies (DOD,
DOE, NIST) or centers (NAMII…)
Co-fund proposals with other programs
Encourage GOALI proposals
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What can the computational
mechanics community contribute
for additive manufacturing (rapid
flexible manufacturing, distributed
manufacturing)?
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Backup Slides
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AM Verification
T (ºC)
Y –
posi
tion (
mm
)
T (ºC)
0.0 1.0 2.0 3.0 4.0 5.0 6.0
0.0 1.0 2.0 3.0 4.0 5.0 6.0
X – position (mm) 1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
Identical Mesh
480 Elements
532 DOFs
Abaqus
In-House
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DSIF - Flexible Forming
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400 µm
Inkjet Printable Graphene for Flexible
Interconnects
• Inkjet printable graphene based on ethyl cellulose
stabilizer in terpineol.
• Low resistivity of 4 mΩ-cm maintained following repeated
flexing and even folding.
Journal of Physical Chemistry Letters, 4, 1347 (2013).
Available from Sigma-Aldrich: Catalog # 793663
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Large-Area Gravure Printable Graphene
Advanced Materials, 26, 4533 (2014).
Collaboration with Lorraine Francis and Dan Frisbie (Minnesota)
Ethyl cellulose stabilizer allows viscosity tuning over
multiple orders of magnitude, enabling compatibility with a
diverse range of printing methods
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Screen Printable Graphene for Flexible
Electronics
Screen printable graphene is compatible with other
materials that are commonly employed in printed/flexible
electronics
Advanced Materials, 27, 109 (2015).
Collaboration with Lorraine Francis and Dan Frisbie (Minnesota)
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Double Sided Incremental Forming: DSIF
Supporting
tool
Forming tool
Part to be formed
Plane of the
undeformed
sheet
Toolpath
• DSIF uses two tools, one on each side, of a
peripherally clamped sheet metal to locally deform
the sheet along a predefined toolpath
• The sum total of the local deformations adds up to
result in a final formed part
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Laser
Depositio
n
(incl.
LENS)
3
4
Powder Bed
Advantages - Build fully dense shapes - Closed-loop, four-axis control - Customizable process parameters for speed,
accuracy and property control - Wide variety of materials, composite deposition
Advantages - Repeatable process control - Build complex shapes
Disadvantages - Poor resolution and surface roughness - Long build times - High laser power required
Disadvantages - Expensive and time-consuming post-
processing - Long pre-heat and cool-down cycles - Contamination of previous molten layer by
environment - Supports required to prevent warping - Uneven deposition and balling due to gradients
in surface tension on powders
Part
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Ti-6Al-4V Porosity and Mechanical Properties
• Increased mechanical strength in the C orientation
• No heat treatment or additional post-processing done on the LENS cube
• hot isotatic pressed (HIP-ed) component testing is ongoing
Specimen Laser
Power (W)
Laser Beam
Diameter
(mm)
Hatch
spacing
(mm)
Layer
Thickness
(mm)
Elastic
Modules
(GPa)
Ultimate Tensile
Strength (MPa)
Elongation
at Break
(%)
Bulk
Porosity %
ASM Grade 5
Ti-6Al-4V,
annealed [1] - - - - 114 950 15 -
Best reported
study [2] 2000 4.0 2.29 0.89 - 1087 10 -
Set A (avg)
800 1.8 1.25 0.95
116 725 0.9 2.2
Set B (avg) 109 820 1.7 2.7
Set C (avg) 144 1015 6.0 2.0
[1]Boyer, R., et al. (1994), Material Properties Handbook: Titanium Alloys, ASM International. [2] Carroll, B. E., et al. (2015), Acta Materialia, 87, 309-320.
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LENS Surface Finish
3
6
• Resolution is usually not better than 0.25 mm and surface roughness more than than 25 microns
• Cannot produce as complex of structures as powder bed fusion processes such as selective laser sintering (SLS)
X
Y
Z Build direction
Scan direction
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Ti-6Al-4V Fractography
3
7
Areas of unmelted powders (45-150 µm) also show higher oxide concentrations – these can
be avoided by optimizing the relationships between hatch spacing, layer thickness, laser
power and scan speed
B
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LENS 316L SS Micro-pores
3
8
• Focused Ion Beam (FIB) takes 2D thin slices to create 3D tomography images and captures micro-pores about a micron or less in diameter
• Micro-pores coalesce to failure; images are provided to mechanical models to simulate uniaxial tensile tests
Ed: 20 J/mm2 Ed : 40 J/mm2 Ed : 50 J/mm2
𝑃 𝑣
𝑑
-laser power (W) -scan speed (mm/s) -laser beam diameter (mm)
z
x
y
y -- scan direction z -- build direction
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LENS 316L SS Microstructure
3
9
• More lamellar-like structures at bottom of the XZ plane due to high cooling rate (showing multiple layers)
• Cellular structures at the XY plane (showing a single layer) – showing low thermal gradient within a layer
Bottom of a part, 50X magnification, (50µm scale bar)
X
Z
X
Y
Center of a part, 100X magnification, (20µm scale bar)
Top of a part, 50X magnification, (50µm scale bar)
Z
X
Z Y
y -- scan direction z -- build direction
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Comparison w/ Abaqus
Te
mp
era
ture
(C
els
ius)
Time (seconds)
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
0.0 0.05 0.10 0.15 0.20 0.25
Max Peak Solution 1433 ℃
Min Peak Solution 1126 ℃
Flux Surface Cutoff Line
Ab
aqu
s S
olu
tion
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Liquid Temp. 1390℃
Solid Temp. 1380 ℃
𝑥103
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Impact Tests of 3 Al housings at 32 ft/sec (3500 lb. impact force)
Cast, A380 1 pc, 38 g
• crack
ed
• buckl
ed
AM,
AlSi10Mg 1 pc, 38 g
• slight
indent
• still
straight
• best
result
Machined
4047 2 pc assy, 45 g
(Baseline Design)
• weld
cracks?
• buckled
Superior Impact Performance
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Modeling Microstructure & Behavior
Brad Boyce
Grain size impacts the uncertainty margins of properties and performance.
Goal: Incorporate material variability in a predictive and probabilistic manner to optimize performance and determine margins
Systematic sampling of grain structure and orientation via modeling and simulations provides greater confidence of margins when only limited experiments are available.
LENS® microstructure with orientation information + Direct Numerical Simulation (DNS) models will enable anisotropic deformation models