TAMU/UCSD/GT Seedling Profile (BIRDSHOT)#

Batch-wise Improvement in Reduced Design Space using a Holistic Optimization Technique

Note

This profile is still under development. The information provided in this profile is based on [Arroyave22, BIRDSHOT22] and interviews with BIRDSHOT team members.

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Fig. 14 Overview of the BIRDSHOT collaborative workflow#

Collaboration#

The BIRDSHOT seedling is a collaboration between eight different teams from four institutions. The collaborative workflow is illustrated in Fig. 14. This is an iterative process.

  • Alloy search and design

    • High-throughput design [Arróyave, TAMU]

  • Processing:

    • Vacuum arc melting (VAM) [Karaman, TAMU]

    • Directed energy deposition (DED) [Vecchio, UCSD]

  • Characterization:

    • Tension [Karaman, TAMU]

    • High strain rate nanoindentation (HSR-NI) [Pharr, TAMU]

    • Laser induced projectile impact testing (LIPIT) [Thomas, TAMU]

    • Spherical indentation [Kalidindi, GT]

  • Data-driven models [Srivastava, TAMU]

  • Optimization [Allaire, TAMU]

Data management#

The BIRDSHOT seedling uses a shared filesystem approach for data management via Google Drive. Information is organized according to a well-defined filesystem hierarchy and naming conventions. The team is working with Contextualize to automate curation, collection and analysis workflows.

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Fig. 15 Filesystem organization and naming conventions (VAM)#

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Fig. 16 Filesystem organization and naming conventions (DED)#

  • Unique code: AAA, AAB, AAC

  • Fabrication method: VAM, DED

  • Production batch: 01-n (VAM is single sample, DED is 8 samples on a single sample holder)

  • Location and sample number:

  • Process: HSR, NI, LIPIT, Spherical indentation, tension, SEM, XRD, EBSD, EDS

Data summary#

Data is organized in the shared folder under three primary folders. Below is a summary of data types in each folder:

  • /data/:

    • NI: nmd, nmdproj, txt

    • SEM: tiff

    • Carta: json

    • Other: xlsx

  • /FeCoCrNiV - Synthesis Sub Project

    • EDS/EBSD: bmp, dat, tiff

    • XRD: hdf5, rasx, scn

    • BBO: m, mat

    • Other: csv, png, pptx, py, xslx

  • /Sample Data:

    • EDS/EBSD: dat, tiff

    • XRD: bmp, par, raw, scn

    • BBO: m, mat

    • Other: csv, pdf, png, ppty, py, tra, txt, xslx

Compiled results and summary tables are generally provided in Excel spreadsheets, however work is being done with the Contextualize data seedling to use custom web-based forms with JSON output stored in the same folder structure. Other types of research assets are present (NI, XRD, EBSD data; source code), but according team members, many research assets are maintained by individual labs or researchers and only summary results shared in Drive.

Alloy search and design#

The alloy search and design process centers on BO for the compositional space. The basic workflow is as follows:

  • Candidate alloys are sent to the team from the two processing routes (VAM or DED) with production job travelers. Each iteration has a unique sample identifier.

  • Preliminary characterization

  • For each iteration, summary results are provided in Excel to BO team

  • Bayesian optimization (Matlab)

    • Objectives: hardness, ultimate tensile strength/yield strength (UTS/YS), strain rate sensitivity

    • 60 experiments per iteration, 3 objectives, executed on TAMU supercompute.

    • 8 samples each from Low and High stacking fault energy (SFE)

    • Original runtime of 42 hours with 400 cores has been reduced to ~1 hour

  • Alloy search across constrained compositional space

Synthesis and bulk property characterization#

  • Target property data informs materials design

  • Batch material production

  • Thermomechanical processing

  • Microstructural property characterization

    • Microstructure and homogeneity evaluation (XRD)

    • Chemical analysis (EDS)

  • Mechanical property characterization

    • Microhardness

    • Tension

Rapid experimental alloy development#

High throughput characterization#

Nanoindendation#

Workflow:

  • Receive samples from fabrication team

  • Obtain elastic modulus and preliminary hardness

  • High strain rate testing using HSR nanoindenter

  • Optical profiling of indents to determine degree of pile-up or sing-in

  • Pile-up/sink-in correction

  • Submit data to computational team and deliver samples to LIPIT group

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Fig. 17 Example spreadsheet with NI optical profiles#

As illustrated in Fig. 17, NI visualizations are included in summary spreadsheets.

Microindendation#

Laser-induced projectile impact testing (LIPIT)#