Physically-Based Modeling in State-Awareness Monitoring Strategies

1. Physically-Based Modeling inState-Awareness Monitoring Strategies David L. McDowell1,2 Regents’ Professor and Carter N. Paden, Jr. Distinguished Chair in Metals Processing Director, MPRL 1School of Materials Science and Engineering 2GWW School of Mechanical Engineering Georgia Institute of Technology, Atlanta, GA 30332 February 19, 2008

2. Background • DARPA AIM program (2001-2003, GEAC) – Development of hierarchical multiscale microstructure sensitive crystal plasticity models for Ni-base superalloys to support objectives of modeling strength and fatigue resistance (PI) • ONR/DARPA Prognosis program (2004-2007, PWA) – Microstructure-sensitive macroscale models for component level design, informed by crystal plasticity calculations for Ni-base and a-b Ti alloys (PI)

3. Related Technoliges • ONR MURI on Integrated Diagnostics (1995-2000) GT, NWU, U. Minn • NSF Center for Computational Materials Design (PSU-GT I/UCRC)

4. Elements of Next Generation State-Awareness • Characterization and “fingerprinting” of as-processed materials and components (including secondary processing) • Modeling of material damage level/state • Strategy for fusion of sensor-model-decision framework that integrates NDE with systems strategies to define the current state and project future state of the system. • Paraphrased from comments of Thomas A Cruse, DARPA/DSO Consultant, on Prognosis – A Vision for 2030

5. Prognosis System • Sensors? • Number, locations and types • Nonunique relation to material state • What is uncertainty of representing state? • What is state? Affected by conception of failure mode – system related Damage State Interrogation http://www.adeptscience.co.uk/htmlemail/mcad_oct_03_images/lg_cutaway-lg.jpg Model Uncertainty Noise, Uncertain Sensor Data • How should the damage state analysis process be configured? • Which models should be employed for diagnosis? • How do we account for process history and initial conditions? Uncertain Prognosis Results / Prediction Life Estimate Models coupled

6. System-Level (Fleet) Decision Support Damage State Interrogation http://www.adeptscience.co.uk/htmlemail/mcad_oct_03_images/lg_cutaway-lg.jpg Model Uncertainty Noise, Uncertain Sensor Data • How should the prognosis results be used for real-time decisions? • Appropriately setting the operating conditions • Redesigning critical parts • System-level Prognosis based on part-level prognosis data Uncertain Prognosis Results / Prediction Remaining Life Models Decisions

7. Triad of Technologies Embedded in Decision Support Framework Premise: This is a system and couplings contribute to uncertainty methods for in situ interrogation of state physically-based models Decision-support framework Materials design for prognosis requirements coupled state-awareness and life models • Treatment of uncertainty is paramount • Probabilistic micromechanics approaches • Robust decision-support framework • Feasibility studies • Justifying impact of prognosis

8. System-Based, Concurrent Product and Materials Design Goal-Oriented Design Methods Continuum Microscale Molecular Quantum CCMD – GT/PSU New area Limitation in Inverse problem System Assembly Cause/Effect Analysis Methods Part G.B. Olson, Science, 29 Aug., 1997, Vol. 277 Material Selection Design methods are available High Degree of Uncertainty Top-down design requirements can include design for damage tolerance and probability of detection

9. Classification of Uncertainty based on Isukapalli’s Definition (Isukapalli, et al., 1998) Uncertainty as a Driver in Hierarchical, Multilevel Decision Framework • Natural Uncertainty (system variability) • Parameterizable: Errors associated with process history, operating conditions, etc. (noise and control factors) • Unparameterizable: random microstructure; randomness of initial conditions of microstructure state • Model Parameter Uncertainty (parameter uncertainty) • Incomplete knowledge of model parameters due to insufficient or inaccurate data; material and interrogation scheme • Model Structural Uncertainty (model uncertainty ) • Uncertain structure of a model due to insufficient knowledge (approximations and simplifications) about a system; NDE interrogation algorithms; definition of what constitutes “state” is a substantial one. • Propagated Uncertainty in a Process Chain (process uncertainty) • Propagation of natural and model uncertainty through a chain of models(e.g., multiscale materials; sequence of hot spots, etc.)

10. Balancing System Uncertainty Uncertainty as a Driver in Hierarchical, Multilevel Decision Framework • Undue emphasis on accuracy and/or fidelity of material structure-property models may be unwarranted if uncertainty of distribution of initial conditions, residual stresses, secondary processing, etc. is prominent • Models aimed at producing probabilistic/stochastic information are desirable  extreme value prognosis (both hot spots and rogue flaws) • Multiple models with different potential mechanisms may be preferable to single, complex model for supporting decisions regarding range of remaining system life • Balanced investment in more comprehensive characterization, monitoring and damage state modeling is warranted • State-awareness sampling and material modeling are strongly coupled, and assessment of coupling effects should be evaluated at the systems level.

11. Microstructure-Sensitive Fatigue Analysis Physically Based Crystal Plasticity Models Fatigue indicator parameters Numerical analyses for representative loading cases Nonlocal Coffin-Manson relation Controlling microstructure features for crack formation and early growth Variation of Fatigue Life Variation of Microstructure

12. Crack Initiation Life Distribution - Polycrystals Distribution of the fraction of cracks as a function of the crack initiation life, Rε=-1, T=650C.

13. ONR MURI on Integrated Diagnostics (CBM) (1995-00) GT, NWU, U. Minn. McDowell, Saxena, Qu, Jacobs, Neu, Johnson, Jarzynski

14. Microstructurally Small Fatigue Crack Growth Ti-6Al-4V Hamm (1998)

15. Microstructurally Small Fatigue Crack Growth Neu and Papp

16. Test System Configuration Preamplifier Digital Wave Signal Conditioning Module AE Waveform Acquisition Fracture Wave Analysis Transducer output 1 3 Compact PZT AE sensor 2 4 Monitoring crack to length of 1 mm (SAW) and up to 3 mm (AE)

17. Needs: Physically Based Models • Identifying and modeling sources of damage and/or degradation, linking physics-based models to engineering models that have utility in prognosis • Computational micromechanics to model variability of microstructurally small crack behavior (formation, propagation) • Effects of load history on evolving damage state and interpretation of sensor signals • Predicting variability of mechanical properties (strength, ductility, fatigue resistance) to stochastic microstructure, initial conditions, environmental exposure, etc. • Nonlinear acoustics or other means of interpreting material state prior to formation of cracks. • Accounting for process history effects on residual stresses, initial damage and defect density, etc. that affect future evolution in prognosis

18. ONR D3D Tool Suite Surface treatment (e.g. shot or shock peening) Thermo-Chemo-Mechanical Processing Primary Deformation processing Microstructure and Inclusion distribution information New Material System Apply fatigue analysis algorithms S Driving force HIPping, altering inclusion orientation, inclusion modification N Depth (mm) Extreme value statistics Explore surface vs. subsurface nucleation Modify process route Improved fatigue performance With QuesTek, LLC

19. System Level Needs – State Awareness • Material modeling should not be done in a “vacuum” apart from systems level considerations. • Methodologies for fingerprinting materials and initial conditions on material state and relation to sensor thresholds/signals • Coupling of models with interrogation schemes via probabilistic, decision-based framework for state awareness • Methods for quantifying level of uncertainty and quantifying propagation of uncertainty (microstructure, model, etc.) in prognosis systems • Shifting the balance of sensing and modeling state via new materials may require materials design Questions?