Introduction to Data Workflows in ICMSE

Transcription

1 Introduction to Data Workflows in ICMSE May 12, 2015 Vasisht Venkatesh David Furrer University of California Santa Barbara May 12th-14th 2015

2 Data Workflows in ICMSE Materials and Process Development Material development Data management plans Characterizing controlling microstructural features Data schema Model Development and Validation Verification and Validation Bayesian updating Production Data Material and Model Monitoring Model Enhancement 2

3 Additive Manufacturing: rapid, low cost, flexible Freeform Fabrication of Parts Powder Bed Technologies Shorter Lead Times Eliminate Tooling Alternatives to Castings New Innovative Designs Direct Metal Laser Sintering (DMLS) Electron Beam Melting (EBM) 3

4 Predicting Material Capabilities Mt l Variability Component Variability Predicted Material and Component Properties Properties Measured Material Properties Predicted Property Variability Controlling Feature and Mechanism MUST be Understood 4

5 Computational Materials Modeling Fit for purpose focus Mechanical Design Material Definition Materials Modeling: Reduced cycle-time Capability optimization Enhanced material definition Life-Cycle Cost Analysis Holistic Design Optimization Mfg Process Definition Mfg Process Modeling: Reduced cycle-time Increased yield and product quality Lifing Analysis Component Modeling & Prediction Component Modeling: Location-specific optimization Optimize part weight/cost Maximized lifing capability Integrated Computational Materials Engineering (ICME) 5

6 ICME Development Path Capture benefits early and often Off-Ramps for Developed Models with FIT-FOR-PURPOSE Capabilities Mechanical Design Material Definition Model Development Physic-based model application flow Life-Cycle Cost Analysis Holistic Design Optimization Lifing Analysis Component Modeling & Prediction Mfg Process Definition 13.0 Materials Modeling: - Material Design / Optimization - Materials Definitions / Design Curves Mfg Process Modeling: CL 8 Forging Process Grain Size Process Design / Optimization - Design Rules and Tools Properties Component Modeling: - Component Design / Optimization - System Design Optimization Interdisciplinary Applications Local Applications Global, Holistic Applications 6

7 Materials Data Perspectives MIL-HBK-5H Materials Engineer Mechanical Engineer 7

8 Material Capabilities Definitions True material capability and property distributions are controllable and reproducible; not random variability. Properties are a fn (chemistry, microstructure, stain, cooling rate, etc.); i.e. pedigree. From D.Furrer, OPTIMoM Conf., 2010 Properties Materials properties are path dependent and are often location-specific. Engineering specifications often treat entire material volume as single, homogeneous property capabilities. Modeling and simulation can help enhance component property capability definitions 8

9 Efficient Testing and Qualification Production Testing Model-Driven Test Plans Evaluation of critical component locations/properties Target sensitive locations that provide information about entire component Reduced testing provides reduced costs and qualification cycle-time Critical All Data to be Captured and Integrated with Current Understanding 9

10 Materials Informatics Chemistry Use of Data and Physics to reduce uncertainty Data Process Microstructure Properties Physics Models Capture and Re-Use Materials Data and Meta- Data ( Digital Thread ) Establish Enhanced Models to Support Future Materials Definitions and Design Functions Quantify Uncertainty of Models and Understanding to Minimize Future Testing Data Mining and Data/Model Fusion 10

11 Materials Data Management DEVELOPMENT DATA Specific data collected, stored & reported Material Data Heat Code Material Spec Chemistry Source Part Number Heat Treatment Machining Source Material Source Expected Results Test Data Test Conditions Temp, Stress, Frequency, etc Test Results Life, Strength, Curves (da/dn vs dk, S-S, etc.) Microstructure Results Administrative Data Charge Numbers Purchase Order Approvers Export requirements Requestor Test Engineer Technical Contact Program Description Tracking Data (barcode) Approvals & Time Stamp Location & Time Stamp Alerts to incoming work Identify bottlenecks Locate specimen Job Status Performance assessment 11

12 Measurement of Microstructure Quantitative metallography is critical to enable understanding influence of limiting features Distribution of features and tails of distributions provide keys to material behavior Historical method to quantify microstructure are no longer acceptable; greater statistical treatment is needed (SVEs, etc.) 12

13 Normalized Life / Strength Grain Size Impacts are Powerful 1 ASTM 8-12 ASTM 6 ASTM 3 U720 data Creep Tensile Strength 0.2 LCF Life Dwell Crack Growth Grain Size (µm) Significant implications for processing choices Source: J.Williams, OSU 13

14 ASTM E112 Grain Size By Comparison Method 14

15 Grain Size Distribution in Waspaloy Material 15

16 Necklaced Grain Structures in Waspaloy Material 16

17 Non-Uniform Grain Size in Waspaloy Material 17

18 Optical and OIM Images of Partially Recrystallized Wapaloy 18

19 Grain Boundary Morphology in Superalloy U o C/sec 0.12 o C/sec 1.3 o C/sec 0.72 o C/sec* Amplitude Periodicity 19

20 Gamma-Prime Size and Distribution in P/M Superalloy 20

21 Gamma-Prime Precipitate Size as a Function of Cooling Rate Gamma-Prime Morphology Gamma-Prime Density Gamma-Prime Size 12.7 o C/sec U o C/sec 0.12 o C/sec 21

22 Gamma-Prime Banding in Superalloy U720 Area Percent of Large Grains 12.7% Area Percent of Small Grains 6.43 Average Size of Large Grains 5.25 um 2 Average Size of Small Grains um 2 22

23 Primary Alpha Size and Volume Fraction in Titanium 23

24 Primary Alpha Size and Volume Fraction in Titanium 24

25 Optical Image Analysis of Ti Transformed Beta Structure M. Dallair and D. Furrer, Quantitative Metallography of Titanium Alloys, Advanced Materials and Processes, December, 2004, pp

26 Micro-Texture in Titanium Alloys 26

27 Digital Thread - Air Force Vision Source: USAF ManTech Presentation (Used with Permission) AIR FORCE VISION: Use ALL AVAILABLE INFORMATION in analyses Use PHYSICS to inform analyses Use PROBABILISTIC METHODS to quantify risks CLOSE THE LOOP from the beginning to the end and back to the beginning 27

28 Usage (e.g. Stress) Turbine Disk Digital Thread ICMSE -Integrated Computational Materials Science & Engineering System Requirements PMDO Preliminary Multi-Disciplinary Optimization Microstructure & Process-Sensitive Materials Definition Data Data Mechanical Data Properties Mechanical Data Properties Mechanical Data Chemistry Properties Mechanical Data Chemistry Microstructure Properties Mechanical Data Chemistry Microstructure Properties Mechanical Meta-Data Chemistry Microstructure Properties Mechanical Meta-Data Chemistry Microstructure Properties Meta-Data Chemistry Microstructure Meta-Data Chemistry Microstructure Meta-Data Microstructure Meta-Data Meta-Data Materials Genome Initiative Mean Lower Bound -3s Typical log Life (e.g. Cycles or TACs) Location-Specific Optimized Component Definition Data Capture / Knowledge Generation 28 Model-Based Manufacturing Definition

29 Digital Thread & Design for Variation Vision Digital Thread Enables More Effective Decision Making Through DFV System Engineering Models Measured & Given Parameters W 20M P 25M P 30M P P 1A P 20M 49M F T 25M T 1A T 20M Performance T 30M P 150M P 125M Modeling GM T 150M T 125M bypass duct liner fan hpc burner hpt lpt afterburner nozzle A 80 P AMB T AMB SPHUM XM Engineering System Design / Analysis N 2 N 1 A45 A 40 FVV CVV W fuel W ab fuel Test Measurements Component Fidelities Fg P3 P 153 FC FNAA P149 WB3 WA2 T3 T149 P 17 FC HPC LHV P1 P124 Wfuel FVV FNAA A 4 T1 T124 N1 CVV HPC A 45 P25 P49 N2 HPT BLD AD 2 T25 T49 A8 LPT BLD AD 4 Multi-Disciplinary Automated Workflows Reqts Capability Engineering Component Design / Analysis Future State: Seamless Digital Thread Enables DFV Reqts Capability Materials and Manufacturing Process Modeling Manufacturing / Supply Chain Goal is 50% reduction in development time with 90% elimination of quality non-conformance 29

30 Design for Variation DFV Enables Reduced Development & Part Costs by Aligning Models, Data, and Producibility 30 30

31 Materials Data Management Coupon Test Data Component Design and Definition Design and Structural Analysis Data Analytics PatternMaster APIs and GUI Interfaces Manufacture and Quality Control Pratt & Whitney Materials Data Management Statistical Data Analysis Development and Design Data MMPDS Production Cert Data MoC REACH Database Automated Transfer and Upload Mechanical Test Data From Internal and Test Vendors Design Data Analysis & Import Production Data Materials of Concern Database Test Jobs Data Analysis Jobs Certification Data at Supplier TeamCenter BoM 31

32 Materials Data Management Data Flow Supplier-1 Firewall Alert Analytics Supplier-2 Supplier-3 PatternMaster Supplier-4 Supplier-5 Rotors Airfoils Structural Castings MFT Site Process Utilized for Digital Data File Transfer 32

33 Materials Data Management Initiative Data Captue Efforts showing Success Data for over hundreds of thousands of serials Data captured for over to-date 10,000 SX serials captured to-date Cert Data GRANTA-MI is Live Production Data Pilot Proving Digital Data Capture Is Possible Data has been Analyzed and Applied to Support and Establish Root Cause on Two Recent Materials QNs 33

34 Materials Data Management Success from Data Analytics Production data has been captured, analyzed, linked to models and applied to enhanced material definitions Castings, Forgings Optimal chemistry range defined for several legacy alloys, which supports required manufacturing control Production Data Capture Data Analytics and Model Development Enhanced Material Definition Quality Release Data Improved Quality and Capabilities through Continuous Learning 34

35 Design for Variation Enabling Multi-Fidelity and Multi-Disciplinary Uncertainty Quantification DFV Minimize Risk Emulator & Bayesian updating software/technology, Training & ESW Optimization Solve for Best Option Automation Build & Run Models Fast Parametric Models Build Models for Design Perturbation Analytical Tools Model the Physics Accurately (Disciplines) Isight & CCE software, Training & ESW Multi-User CAD, multi-fidelity geometry modeling Multi-fidelity physics-based models Computing Hardware & Software to Run Engineering Models (IT) Investment in high speed computing Internal Investment in Key Enablers Driving Broad Implementation of DFV 35

36 Computational Materials Modeling Enhance specific materials capabilities Design for cost and manufacture Rapid, robust materials definitions High Temp OMC Cases / Structures Lay-up RTM Autoclave Stiffness Damage Gears Forging Austenitic Grain Size Carburization RCF IBRs Machining Peening Residual Stress Texture TMF Hybrid Structures Forging Polymer Interface Properties Corrosion CMC Airfoils / Structures Turbine Blades Lay-up RTM Autoclave Damage Cast Cases / Structures Solidification Phase Equilibria Heat Treatment Joining Residual Stress Casting Coating Alloy Design Freckle/Porosity/RX TMF Coating Damage Corrosion Rotors Billet Conversion Forging Heating Treatment Machining Alloy Design Residual Stress Grain Size Precipitation Interfacial Energy Tensile Property Yielding Behavior Many Applications for Materials and Process Modeling within Turbine Engines 36

37 Conclusions Prediction of material and component properties is a major element of ICME Understanding of weakest microstructural links and associated mechanisms is required Traditional characterization methods not acceptable for understanding limiting features relative to statistics and tails of distributions Appropriate length scales of SVEs required based on feature and mechanism Big Data and data analytics are critical elements of ICME 37