Transcript Project Objectives - CPS-VO

Cyber Enabled Manufacturing
Systems (CeMs) for Small Lot
Manufacture
University of Texas at Austin
NSF CNS-1239343 PI: J. Beaman
Co PIs: A. Mok, R. Moser, J. Murthy
Presenter: Daniel Moser
Outline
• Project objectives
• Particle filter based control for Vacuum Arc
Re-Melting (VAR)
• LAMPS project overview
• Selective Laser Sintering (SLS) modeling
• Conclusions and future work
Project Objectives
 Develop physics-based cyber-enabled process control
for small-lot manufacture
 Selective Laser Sintering (SLS) process as exemplar
 Project focus:
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High-fidelity multi-scale process models
Model reduction for real-time control
Uncertainty quantification and decision making
Demonstrate on exemplar system
Previous Work: Vacuum Arc Remelting
• Quality of high-grade
superalloys improved by
re-melting
• DC current used to melt
metal electrode
• falls to bottom of copper
crucible and builds up
ingot
• By performing procedure in vacuum, volatile compounds
are removed
• Important to minimize solidification defects due to high
product cost
• Need to control current to obtain desired melt pool profiles
Stochastic Non-linear Model
• Multi-physics models are used to study solidification in VAR
including: heat transfer, fluid mechanics, electromagnetics,
mass transfer, and phase transformations.
• High order models cannot be used in control scheme due to
computational costs, reduced order models needed
• State of system is estimated from measurements using
reduced order models
• Bayesian framework to handle uncertainties in models and
measurements
• Allows estimation of PDFs of the state variables
Results
• Goal: Apply similar model-based control to SLS
process
Acknowledgements: Lixun Zhang, Felipe Lopez
Selective Laser Sintering
• Method for creating solid
parts directly from CAD
model
• Builds up a solid 3-D object
by selectively fusing
successive layers of powder
• Successful build requires
proper control of subsystems
Subsystems to control:
• Powder supply
• Powder supply heating
• Powder spreading
• Layer thickness (part piston height)
• Chamber temp/Build box temp/Build surface temp
• Laser scan pattern
• Laser Power, Speed, Spot Diameter
• Part Cooling
LAMPS* Project Objectives
• Design and build high-temperature SLS for
PEEK/PAEK/PEKK class materials: (Process
temps as high as 350 °C)
• High Tensile Strength
• Good resistance to fatigue, creep, flammability,
and moisture
• Current methods for use with SLS are expensive
and limited
• Produce tensile specimens for mechanical
property assessment
• Provide In-situ measurement access
• Open Architecture Software (access to all
process parameters)
• Host improved process controls for polymer
SLS
* Laser Additive Manufacturing Pilot System
LAMPS Design - External
LAMPS Design Section View
Acknowledgements: Scott Fish, Steven Kubiak, Adam Bryant,
Walker Wroe, John Booth
Computational Model
• Quality of SLS parts sensitive to input processing
parameters
• Optimal parameters often not know
• Experimentation and testing required for new materials
and/or geometries
• High-fidelity computational model can reduce need for
testing and experimentation
Continuum SLS Model
• Powder particle diameters ~10μm
• SLS parts ~1m
• Resolving individual particles computationally unfeasible
• Volume averaging approximates powdered materials as
continuum to make the problem tractable
• Continuum model:
𝜕𝑇
2𝑃
𝑥 − 𝑥𝑙 (𝑡) 2 + 𝑦 − 𝑦𝑙 (𝑡)
𝜌𝑐𝑝
= 𝛻 ∙ 𝑘𝛻𝑇 + 𝜀
𝛽𝑒𝑥𝑝 −2
𝜕𝑡
𝜋𝜔 2
𝜔2
2
− 𝛽𝑧
• Layer additions accomplished by changing cell material
properties from air to powder
Continuum Model Results
Model Comparison vs
Experimental Results
Input Parameters:
• PVA powder properties (ρ=1290
kg/m3, Cp=1546 J/KgK, k = 0.2
W/mK)
• 4W Laser with 0.4mm diameter
• Powder is heated for 0.2ms by a
stationary laser and
measurement is taken 1ms after
laser is turned off
• Peak temperature measured
was ~70K over ambient
Uncertainty Analysis
Property
Powder Density
Powder
Conductivity
Powder Specific
Heat Error
Average Particle
Diameter
Simulation Time
Symbol
ρ
k
Distribution
Uniform (1270-1310 kg/m3)
Uniform (0-0.3 W/m/°K)
Prop.
εc
Normal (μ=0, σ=200 J/kg/°K)
Dp
k
Dp
Normal (μ=0.15, σ=0.015 mm)
tfinal
Normal (μ=1.2, σ=0.25 ms)
εc
tfinal
ρ
Uncertain Parameters
Sensitivity Std.
Dev.
15.2 °K/( J/kg/°K)
14.5 °K/m
5.66 °K/(W/m/°K)
4.64 °K/ms
0.4 °K/( kg/m3)
Sensitivity Results
0.045
0.04
0.035
0.03
Probability
Sensitivity
Mean
47.8
°K/(J/kg/°K)
43.8 °K/m
12.0.
°K/(W/m/°K)
7.56
°K/ms
1.99
°K/( kg/m3)
0.025
0.02
0.015
0.01
0.005
0
40
50
60
70
80
90
100
110
120
130
Temperature (K)
Mean = 77.4K, Dev = 10.7K
Prob. Distribution of Peak Temperature
140
Bulk Powder Properties
• How to determine emissivity (ε) and extinction coefficient (β
- approximated as 1/Dp in the continuum model)?
• Values known for solid material but different for powder
• Large uncertainties in bulk properties create large
uncertainties in model outputs
• Calculate bulk properties using a particle-level model to
reduce uncertainties
Particle Level Model
• Powder bed configurations
generated using a discrete
element method
• Particles modeled as spheres
• Interactions are handled using a
spring-dashpot model
• Particles randomly dropped into
domain and allowed to settle
• Optical interactions modeled
using ray tracing
• Results averaged across
many different structures
Example Packing Structure
Particle Level Model Results
Prop.
ε
β
Mean
0.46
0.58
1/(Particle Radius)
Std. Dev.
0.01
0.02
Calculated Optical Properties
Conclusions and Next Steps
Conclusions:
• Continuum system-level models can be coupled with
particle-scale models to simulate entire part builds while
capturing powder characteristics through bulk material
properties
Next Steps:
• Couple part build history to microstructure evolution,
thermal stress development, and final part shape
• Develop optimization to determine ideal processing
parameters for given materials
• Develop reduced-order computational model for use in
real time control of SLS machine