© asm international · 2015. 12. 17. · lessons learned • best practices for capturing and...

1© ASM International


• Computational Materials Data Network

• Materials Genome Initiative (MGI)

• SMDDP project launch and implementation

• Live database demo

• Lessons in database development

• Future work & potential follow-on projects

• Summary

Webinar outline


ASM is a trusted

provider of critical

materials data and

a natural choice

to support the

materials data

community in its

efforts to establish

best practices for

capturing, organizing,

and sharing digital data.

Data heritage


In 2012, ASM launched the

Computational Materials Data

Network to assist the MGI/ICME

community with its computational

materials data challenges and needs.

Adapted, Office of Science and Technology Policy

CMDN focus: Digital data


• Advance materials data

management practices and

techniques

• Serve as a hub for the

collection and dissemination

of materials data

• Support the MGI/ICME

community

• Enable faster materials and

process innovation

Goals and objectives


Society strengths


• Develop new materials faster

• Revitalize U.S. manufacturing

New tools, new practices,

new ways of working together

Adapted, Jim Warren, NIST

Supporting data-driven innovation


Source: Jim Warren, NIST

Data management challenge


Data repository


Increasing organization


Open curated

repositories


Increasing scale


SMDDP Repository

Adapted, Jim Warren, NIST

Demonstration project


• Establish well-pedigreed and curated demonstration datasets for non-

proprietary metallic structural materials over multiple length scales.

• Work with NIST and the materials data community to develop

materials data schema and ontologies.

• Develop and carry out a series of test problems that represent

relevant use cases for the repository.

• Make data open to the materials data community for use in data

analytics, modeling, and educational activities.

• Actively engage the materials data community and widely

disseminate the findings from the project.

• Develop and implement data capture and curation procedures that

can serve as models for other data repositories.

SMDDP project objectives


ASM InternationalCMD Network

NIST

Kent State UniversityCenter for Materials Informatics

Granta Design/MDMI

Georgia Tech

• Hosting and curation

• Project management and outreach

(Nexight support)

• Data acquisition for “upstream” data

• Schema and ontology development

• Test and evaluation support

• Open access repository development and

support

• Database structure and development guidance

• Import and export interface development

• Aluminum sample processing

• Microstructure and mechanical data

measurements

Project roles


Phased rollout


SMDDP data path


http://www.asminternational.org/web/cmdnetwork/projects/structural-materials/project-homepage

Project homepage


https://materialsdata.nist.gov/dspace/xmlui/

Accessing the SMDDP repository


https://materialsdata.nist.gov/dspace/xmlui/handle/11256/419

Data collections


SMDDP database homepage


Lessons learned

• Best practices for capturing and sharing data

– Chart a strategic course and identify critical pathways

– Spend time upfront on database organization and schema development

– Make sure others can repeat your work by providing sufficient pedigree and provenance

– Employ standard file formats

– Don’t overlook data citation and licensing

• Limitations of traditional search technology

– Benefits of ontologies, registries, and semantic search

• Currently available data are not MGI/ICME friendly


Critical pathways


Organizational refinements

• Bottom up organization; hub-and-spoke

linkages

• Similar pattern followed at each level

• Elements

• Systems

• Alloys

• Records renamed to create more

consistent pattern

• [Content description] ([Source label])

• Reduced unnecessary levels in tree

structure hierarchy

• More consistent treatment of sources


• Relating the information reduces any

need to repeat data

• Selection of related data is simplified

• Experimental and computational data can

be more easily identified and compared

Relational database schema

Source: Tom Searles, MDMi


• Material history is the foundation for understanding and employing

experimental and computational data

– Provides immediate comparison between experiment and simulation, enabling

correlation and model prediction assessment

• Capturing this information facilitates repeatability and reproducibility

– Enables re-assessment of a model’s accuracy as the experimental data set grows

• Missing traceability devalues data

– Renders data unusable beyond initial scope

– Limits modeling and validation of data

• No aspects of material pedigree and provenance should be overlooked while

collecting and storing material information

– Determining this information post-project is often extremely difficult if not

impossible

Traceability: Pedigree & provenance

Source: Tom Searles, MDMi; Steve Arnold, NASA-GRC


• Consistency is key

– Keeping a standard format reduces the effort necessary to

collect and store data

• Preference is standardized ASCII or Excel files

– Enables rapid collection of information

– These formats are preferred by most

– GRANTA MI allows for rapid data collection in these formats

• Data should be written directly into the data repository

File formats

Source: Tom Searles, MDMi


Ontologies and semantic search

Source: Sam Chance, iNovex, matonto.org


If a community uses a shared vocabulary to annotate its

data, then interoperability can be greatly enhanced by

eliminating ambiguity – e.g., resolving synonyms (elastic

modulus, Young’s modulus), connecting properties to

relevant test standards (ASTM E111).

• The CMD Network is contemplating a pilot project for an open materials

vocabulary in connection with the NIST-ASM Structural Materials Data

Demonstration Project.

• ASM has thousands of ASM Handbook terms and definitions.

• We are engaged in discussions with ASTM about potential approaches to

harmonizing ASM and ASTM terms and definitions.

Common materials vocabulary


SMDDP

Looking toward the future

Develop and employ data management best practices


In-process data for modeling

Objective: Define how companies and organizations can work together to

expand availability, improve access, and reduce the cost of obtaining

pedigreed data for modeling manufacturing processes and their effects on

material properties and performance.

Outcomes:

• Identified and ranked the types of modeling data that are most

challenging for organizations to obtain.

• Developed options to facilitate collaboration whereby participants

can obtain and share in-process materials data for modeling.

• Published workshop report (available at cmdnetwork.org)

• Refining options for collaboration framework

In-Process Materials Data for Modeling WorkshopAugust 11, 2015; Dayton, Ohio


Open AM database


NIU-MSAM project


NIST Materials Data Curation System

• Written in python• Backed by MongoDB• SPARQL Query interface• XML-based Schema• Table input

Features:• Ability to store templates• Schema management tools• REST API interface • Schema ComposerSource: Robert Hanisch, NIST


http://rd-alliance.org/

Source: Robert Hanisch, NIST


Modeling use case

Source: Greg Olson, Northwestern University


Precipitate growth experiment

• Obtain baseline microstructural data on a sample of 6061-T651• Record data related to constituent particles, grain structure, and texture • Acquire TEM images of precipitate phases to correlate precipitate microstructure

with tensile properties

• Processing details for over-aging Al samples• Expose 6061-T651 samples to four different elevated temperatures:

i. 2 hours at 400Fii. 2 hours at 525Fiii. 2 hours at 650Fiv. 2 hours at 775F (comparable to annealed –O temper)

• Quantify amount of Mg2Si phase after each treatment• Measure mechanical properties at each condition

Source: Warren Hunt, Nexight Group


Raw microstructure data

Source: Surya Kalidindi, GaTech; Yaakov Idell, NIST


• Alloy composition

• Temperature as a function of time

• Thermodynamic data (Gibbs energy functions)

• Kinetic data (diffusion mobilities)

• Interfacial energy

• Dislocation density

• Grain size

• Microstructure information related to nucleation sites

Additional data required

Source: Carelyn Campbell, NIST


Microstructure data


In summary

• Created a repository and database for Al 6061 data – mechanical,

diffusion, phase, and microstructure data

• Created and refined data schema, metadata, citation, and licensing

protocols

• Developed and tested data importers and exporters

• Developed and conducted a series of heat treatments to collect data to

analyze process-structure-property relationships

• Gathered additional microstructure data to compare with precipitation

simulations and begin modeling the effect of heat on tensile strength

• Looking for partners and collaborators to share the data and explore

additional uses and application ideas


Thank you!

© asm international · 2015. 12. 17. · lessons learned • best practices for capturing and...

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