ICME: Databases and Data Sciences

Transcription

1 ICME: Databae and Data Science Surya R. Kalidindi George W. Woodruff Scool of Mecanical Engineering Scool of Material Science and Engineering Scool of Computational Science and Engineering Georgia Intitute of Tecnology

2 Quetion How can we move beyond artificial neural network and extract te well-idden contitutive equation for propertie uc a trengt and fracture tougne? Employ a combination of modern data analytic (e.g., macine learning) and rigorou tructure quantification (e.g., n-point tatitic) How can we utilize domain knowledge along wit data cience for better outcome? Modern data analytic can andle ti (e.g., Bayeian approace) Wat are te mot effective and computationally efficient metod for creating reduced order model for capturing proce-tructure-property linkage (i.e. core material knowledge ytem or databae)? Already anwered above

3 Quetion Digital Data: How can we automatically curate data? How can we bet capture data and metadata from cientific intrumentation? Federated ytem for data wit centralized repoitorie for metadata New tool/practice are needed to capture/extract metadata (e.g., arcival file format, mobile application, data and code repoitorie, provenance tracking) How do we package material knowledge for effective integration wit currently ued commercial manufacturing proce imulation tool? Reduced-order repreentation of proce-(ierarcical) tructure-property linkage wit quantified uncertainty

4 Proce-Property Linkage 437 data point Agrawal, Depande, Cecen, Gautam, Coudary, Kalidindi

5 Microtructure Quantification Te extracted dataet ow a microtructure, not te microtructure Need a framework for defining a ierarcical et of tatitical meaure of te microtructure at any elected lengt cale: n-point patial correlation

6 2-Point Correlation Microtructure 2-point auto correlation Adam, Kalidindi, and Fullwood, Microtructure Senitive Deign for Performance Optimization, 2012

7 2-Point Cro Correlation Tree Pae Microtructure Red-Green Cro Correlation Number of 2-point correlation i very large

8 Microtructure Statitic f f f 3..., 2 ' 1-point correlation (Volume Fraction) r, ', ' ' 2-point correlation r, r' 3-point correlation Conveniently computed uing FFT algoritm PCA: Hypotei: PCA weigt of n-point tatitic provide objective meaure of microtructure

9 Microtructure Databae HT1-20 micrograp HT2-28 Micrograp HT3-32 micrograp Data from H. Fraer group at OSU HT4-36 micrograp HT5-32 micrograp

10 Viualization of Databae Red=HT1 Blue=HT2 Green=HT3 =HT4 Magenta=HT5 Eac point correpond to a microtructure dataet. Dataet from te ame eat treatment are own a a ull. Volume of te ull can be related directly to te variance in tructure between dataet. Euclidean ditance i a metric of imilarity or difference between ample Quality control application Kalidindi, Niezgoda, Salem, JOM, 63, pp , 2011

11 Structure-Property Linkage Cortical Bone Dataet Randomly Generated Dataet Nutell Dataet Total 711 Dataet V f = 0.01 to 0.45 FE Simulation performed to evaluate effective value of C11

12 Structure-Property Linkage 3 PC Quadratic Fit (10 Fit Parameter)

13 Structure-Property Linkage Effect of Hard and Soft Particle on Effective Platic Propertie (Gupta, Cecen, Goyal, Sing, Kalidindi) ANSYS ANSYS microtructure

14 Proce-Structure Linkage Final Initial

15 Data Mining for Scale-Bridging Localization ε ( x) a( x) ε ( x) Homogenization Scale-bridging need to accommodate bi-directional flow of information, i.e. bot omogenization and localization need to be addreed. Sould be able to exercie cale-bridging wit minimal computational effort.

16 MKS Approac p m m Microtructure ignal (volume fraction of in ) Repone ignal Microtructure Evolution ignal t t p p p m m m m m m m ˆ ˆ ˆ ˆ p α α p t t t t t tt t t t... m m m

17 Validation of Firt-Order Influence Kernel for Rigid-Platicity Contour of elected train rate component FEM y y 1 2 MKS Te FEM analyi required 94 our on te Oio State upercomputer, wile te MKS reult are obtained in 32 econd uing a regular dektop PC 2 Kalidindi ISRN Material Science, 2012

18 Application to Polycrytal

19 Application to Crytal Platicity Fat crytal platicity computation at te grain cale (for individual grain or region witin a grain) Direct calculation 108 econd Spectral Databae 0.7 econd Knezevic, Al-Harbi, Kalidindi, Acta Materialia, 2009 Claical CPFEM SD-CPFEM SD-CPFEM can peed up te computational time by about 40 time

20 Property Cloure Uing Spectral Databae Taylor Model Knezevic, Kalidindi, Mira, IJP, 2008

21 Proce Deign a) b) c) FCC Metal Saffer et al., IJP, 2010