Transcript pnagy/NDE/AFOSR Prognosis - No Slide Title

An Unified Approach to Structural Health
Management and Damage Prognosis of Metallic
Aerospace Structures
Aditi Chattopadhyay
Department of Mechanical and Aerospace Engineering
Arizona State University
Prognosis of Aircraft and Space Devices, Components, and Systems
Air Force Office of Scientific Research
University of Cincinnati, Cincinnati, Ohio
February 19 and 20, 2008
Grant Number: FA95550-06-1-0309
Program Manager: Dr. Victor Giurgiutiu
Aditi
Chattopadhyay
Antonia
Papandreou
•Smart structures
•Signal
processing
•Sensing &
Information
Processing
•Detection &
Estimation
•SHM
•Multiscale
modeling
•Mechanics of
composites
James B. Spicer
•Materials process
monitoring & control
•Ultrasonics
•High-temperature
characterization
•MDO
Roger G. Ghanem
Pedro Peralta
•Fracture & fatigue
•Composite
materials
•FEM
•Continuum
mechanics
Douglas
Cochran
•Statistical
signal
processing
•Theory of
sensing
•Mathematical
modeling
MURI
Structural Health
Monitoring &
Prognosis of
Aerospace
Systems
•Risk assessment
•Stochastic
mechanics
•Computational
mechanics
•Inverse problems
and optimization
Dan Inman
•SHM
• Wireless sensing
and damage
assessment
•Membrane optics
2
MURI RESEARCH TEAM
ASU Team
Academic Professionals: Jun Wei, Narayan Kovvali
Graduate Students : Debejyo Chakraborty, Clyde Coelho, Chuntao Luo, Subhasish
Mohanty, Donna Simon, Sunilkumar Soni, Rikki Teale, Christina Willhauck
USC Team
Academic Professional: Maarten Arnst
Graduate Students : Maud Comboul, Sonjoy Das, Arash Noshadravan
JHU Team
Academic Professional: Seyi Balogun
Graduate Students : Lindsay Channels, Travis DeJournett
VT Team
Academic Professional: Benjamin L. Grisso
Graduate Student : Mana Afshari
OBJECTIVES
•Computationally efficient multiscale
modeling techniques for characterizing the
damage state of a material (including
nucleation and growth)
• Damage detection and classification
techniques for sensor integration and
instrumentation
• Prognosis capabilities for predicting failure
probability and remaining useful life
• Testing, validation and application
TECHNICAL APPROACH
•Physically based models to characterize
damage nucleation & growth
•Characterize wave propagation in hotspot
•Optimally integrate sensor network
•Waveform design & damage detection
•Sensor management schemes for
detection/classification
•Stochastic models to account for uncertainties
•Estimation of remaining useful life
•Validation on test structures
BASIC RESEARCH ISSUES
• Connect microscopic damage to macroscopic
scale monitoring
• Sensor sensitivity
• Sensor/host structure coupling
• Hierarchical information management
• Hybrid approach for life estimation
• Precursor to damage/first failure to inspection
BENEFIT TO DOD/INDUSTRY
• Improved techniques will facilitate assessment of
health of metallic aircraft structures
• Project outcome will help surmount some of the
technical challenges, complementing ongoing
activities at AFRL
• Research results will help establish improved
IVHM systems
• Future aircraft systems can benefit from
integration with prognosis programs focused on
current aircraft.
• Advancements in damage analysis, detection &
classification are useful sustainable
infrastructure and electronic system monitoring
TASK DESCRIPTIONS AND PERSONNEL
A. Chattopadhyay
P. Peralta
James Spicer
A. Chattopadhyay
A. Papandreou-Suppappola
Daniel Inman
Douglas Cochran
A. Chattopadhyay
P. Peralta
Roger Ghanem
Task 1
Task 2
Task 3
•Material Charecterization
•Multiscale model to
predict damage nucleation
& growth
•Optimal sensor placement
•Detection
•Signal processing
•Diagnosis & classification
•State awareness
•Life prediction
Task 4 (All PI’s)
Testing, validation and
applications
AFRL / VA
Boeing Phantom Works
•
•
•
DOD COLLABORATIONS AND TRANSITION TO
REAL SYSTEMS
Collaboration with AFRL:
• Mark Derriso, Structural Health Assessment Team Leader, AFRL/VA
• Provides data from AFRL experimental set-ups
• Frequent meetings with Mark and his team: discuss MURI progress
and relevant AFRL problems needed to help transition of our work to
real systems
• Meetings with Jim Larsen (AFRL/MLLMN) and Kumar Jata (AFRL/MLLP)
Collaboration with Boeing Phantom Works (Eric Haugse)
• Hotspot Program with AFRL (involves actual F-18 testing in Arizona for
transition to real systems).
Participants: AFRL, Boeing Phantom Works, Accelent Technologies,
Metis Design
• HotSpot Monitoring Program teleconference (bi-weekly)
Advisory board committee provides feedback:
• Members from AFRL, US Air Force Academy, United Technologies
Research Center, Boeing, Next Generation Aeronautics, Lockheed Martin
Aeronautics Company, National Transportation Safety Board, NRL, Naval
Surface Warfare Center, NASA GRC, NASA LaRC, NASA ARC, US Army
ARDECOM, Los Alamos National Lab.
TECHNICAL APPROACH
Physically-based Multiscale
Modeling
Microstructure
Reconstruction
Material
Characterization
Multiscale
Modeling
Representative
Microstructure (FEM)
Metallography
Microscale
Damage Initiation
RVE for Grains/ Particles
3D Grain/ Particle size
distributions
Short Crack Growth
in the Mesoscale
Structure Level
Fatigue Simulation
MATERIALS CHARACTERIZATION
Multiscale Material Characterization

Strain fields ahead of fatigue cracks in wrought Al alloys: in-situ testing and
DIC
Load Direction
Load stage
and Rolling
Direction (RD)
Crack tip
Loaded specimen

Nanoindentation of precipitates in wrought Al alloys
Crack tip
MATERIALS CHARACTERIZATION
Microstructure Reconstruction and Representation

Use Electron Backscatter Diffraction (EBSD) along with serial sectioning: 2-, 2.5- and 3D
2-D

2.5-D
3-D
“Artificial” microstructures are also being generated
Same grain size (100 µm) different grain size distribution
Large (300 µm) grain size
MATERIALS CHARACTERIZATION
Microstructurally Explicit Finite Element Models

Use microstructure representation and meshing tools: defects can be included

3-D
2.5-D
2-D
Results show effects of microstructural variability on local fields
2-D
3-D
INTERACTION OF RELEVANT SCALES IN MULTISCALE
MODELING
Homogenization
Macro Scale
Micro Scale
Material
Characterization
Damage Parameter
0.03
Localization
0.03
0.02
0.02
0.01
0.01
0.00
0.00
Crystal Orientation
10.00
15.00
20.00
25.00
30.00
35.00
Time (s)
Damage Parameter
Long Crack Propagation
250
Single Crystal
Structure
Polycrystal
Structure
Orientation
Distribution &
Properties
300
Stress (MPa)
Crystal
Properties
5.00
Void Model
2-D Slice
2.5-D
Representation
Meso Scale
200
150
100
50
0
0
0.002
0.004
0.006
0.008
0.01
Strain
Crack
Initiation
Short Crack
Propagation
Hardening Parameter
Component
3-D
Representation
Wave Propagation
11
FATIGUE ANALYSIS (SINGLE CRYSTAL)
400
Stress (MPa)
300
200
100
0
-0.004
-0.003
-0.002
-0.001
0
0.001
0.002
0.003
0.004
-100
-200
-300
-400
Strain
Stress-strain response

Capture crystal orientation

Fatigue hardening & saturation

Accumulative shear strain
Mesoscale
Accumulated shear strain
Mises stress distribution
8.0E-02
6.0E-02
4.0E-02
2.0E-02
0.0E+00
0
2
4
6
Number of cycles
8
12
Stress (MPa)
MESOSCALE STRUCTURE
500
400
300
200
100
0
-100
-200
-300
-400
-500
-4 -3 -2 -1
0
Strain
Grains
OIM (Orientation
Imaging Microscopy)
Scan
OOF
1
2
3
4
X103
ABAQUS
& UMAT
13
TECHNICAL APPROACH
Methods for In Situ Interrogation and Detection
Sensor design,
network and placement
Damage detection
and classification
Nonlinear ultrasonic
damage characterization
Sensing multi-scale damage
with impedance, vibration,
& Lamb wave based methods
Time-frequency & statistical
damage classification: AFRL
TPS, ASU bolted-joint data
Mesoscopic ultrasonic
techniques for assessment
of material microstructure
FEM based analysis of
macro- length scale damage
with virtual sensors
Bayesian sensor fusion
of data received from
multiple distributed sensors
RESULTS: NONLINEAR ULTRASONICS
Team Integration
JHU Ultrasonics Group (Spicer)
Surface
displacement

Ultrasonic
generation
data
ASU SP Group (Papandreou, Cochran)
Receiver
Ultrasonic displacement
measured at the epicenter
2.4 mJ
Amplitude of Model
0
-0.01
-0.00159
-0.02
-0.00318
Model
-0.00477
-0.03
-0.04
Lens
Oscilloscope
Amplitude of Unfatigued 6061
Measurement
0
Nd:YAG
532 nm
continuous
-0.00636
0
400
800
1200
1600
-0.05
Time (ns)
Spectrogram
Iris
Michelson type
interferometer
Nd:YAG
1064 nm
9 ns pulse
_
Lens
Sample on
translation
stage
+
Mirror
Piezoelectric
mirror mount
Experimental Schematic for
Laser Ultrasonic Investigations
Stabilization
circuit
RESULTS: SUPPORT VECTOR MACHINE BASED
DAMAGE CLASSIFICATION
Sample Tested
Results
Sensor
Actuator
Team Integration
Lug joint: typical structural hot spot
ASU SP Group (Papandreou, Cochran)
features for SVM
ASU Modeling Group (Chattopadhyay)
RESULTS: TIME-FREQUENCY CLASSIFICATION
Collaboration with Mark Derriso (AFRL/VA)
• PZTs attached to bolted
square aluminum plate
• PZT-1 used for
transmitting 0-1.5 kHz
chirp
• Signals received at PZT2, PZT-3, and PZT-4
• Damage Class definition:
- Class 1 = Bolt 1 at 25% torque
- Class 2 = Bolt 2 at 25% torque
- Class 3 = Bolt 3 at 25% torque
- Class 4 = Bolt 4 at 25% torque
- Class 5 = All bolts at 100% torque
(fully tightened/healthy case)
S. Olson, M. DeSimio, and M. Derriso, “Fastener
Damage Estimation in a Square Aluminum Plate”,
Structural Health Monitoring Journal, 2005
Confusion matrix
(HMM based damage classifier)
0.80500
0.05500
0.13500
0
0.00500
0
0.98000
0.00500
0
0.01500
0.05500
0.08000
0.86500
0
0
0.00500
0.02000
0
0.97000
0.00500
0
0
0
0.00125
0.99875
TECHNICAL APPROACH
Prognosis via State-Awareness
and Life Models
Probabilistic Data Driven
Prognosis Model
Prediction of crack growth and
plastic zone parameters by
Gaussian Process Model
Fracture Mechanics
Based Physics Model
System Identification
Prognosis Model
Prediction of effective stress
intensity factors that account
for closure effects
Vibration and wave based
system identification for
damage state estimation
Hybrid
Prognosis Model
R
U
L
E
GAUSSIAN PROCESS DATA DRIVEN APPROACH
Based on high dimensional kernel function
Uncertainty quantified using Bayesian approach
History as training distribution
Predicts new mean damage and associated variance
Predicts possible collapse point if new predicted variance
exceeds threshold flag
Damage Index (Crack Length)
•
•
•
•
•
k
N
N+1
Flight Cycle
DATA DRIVEN MODEL PREDICTION
Load
Spectrum
Single Variate
Model
Prediction
HYBRID PROGNOSIS APPROACH
Data driven model
for calculating
plastic zone
constraint factor
Results From Pure Physics Based Model
Incremental crack
length from
physics based
model
Results From Hybrid Model
TECHNICAL APPROACH
Testing, Validation & Application
Calibrate and validate
modeling methods
Material characterization,
multiscale model and
state awareness model
Sensor network and
placement
•Detection
•Signal processing
•Diagnosis &
classification
Application to
Structural Hotspots
AFRL/VA, Boeing
Structural Hotspot
Program
EXPERIMENTAL OBSERVATIONS
Sample 1 (Polished)
Cycles to 380,621
failure (110 – 1100 lbs)
Sample 2 (Sand Blasted)
823,537
(110 – 1100 lbs)
Sample 3 (Sand Blasted)
>3 Million
(80 – 800 lbs)
 Life of sample 2 about twice of sample 1 under similar loading condition
 Two distinct damage nucleation sites for sample 2
 Failure mode - High cycle fatigue from shoulders for sample 1 & 2
- Very high cycle fretting fatigue from pin hole
Induced Stresses Influence Fatigue Life and Failure Patterns
GUIDED WAVE IN LUG JOINT
Healthy
Damaged
FUTURE WORK
Task 1:
• Predict damage nucleation & propagation using modified fatigue damage criteria.
• Simulate sensor signals & study their interaction with cracks using distributed point
source method (DPSM) – a wave based approach.
Task 2:
• Adaptive signal processing and classification using active and multi-task learning
methodologies.
• Use of data from new sensors and physics based FEM modeling to train damage
detection and classification algorithms.
Task 3:
• Formulate multivariate prognosis models that incorporate physical-based models to
account for load sequence effects.
• Incorporate material and sensor signal variability into prognosis framework.
• Develop a prognosis approach for crack nucleation based on "virtual sensors" (output
from multiscale modeling) to estimate life spend to grow "detectable" damage.
Task 4:
• Perform testing on instrumented samples with complex geometry (lug joints, bolted
joints) to gather statistical information on failure modes, sensor performance and to
collect data for model validation (integration with Tasks 1, 2, and 3).
• Develop a test article for use with the biaxial load frame to obtain statistical
information under both complex geometries and complex loading (integration with
Tasks 1, 2, and 3).