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Record W4413216951 · doi:10.1115/gt2025-152958

Learnings in the Qualification of ABD®900AM for Turbine, Aerospace, and Energy Applications

2025· article· en· W4413216951 on OpenAlexaff
John Shingledecker, Alex Bridges, Nikki Harless, Richard Grylls, Zara Hussain, Shankar Srinivasan, Marco Musto, Kenneth Kroenlein, James E. Saal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsSiemens (Canada)Smarter Alloys (Canada)
Fundersnot available
KeywordsAerospaceComputer scienceStandardizationWork (physics)SuperalloyMaterials scienceMechanical engineeringProcess engineeringManufacturing engineeringAlloyMetallurgyEngineering

Abstract

fetched live from OpenAlex

Abstract ABD®-900AM is a highly-printable, gamma prime strengthened superalloy purposely designed for powder bed fusion (PBF) additive manufacturing (AM) with broad applicability to high-temperature gas turbine, aerospace, defense, and energy applications. To accelerate the adoption of the alloy, EPRI, with support from a team of AM processing and material data informatics experts, has initiated a first-of-a-kind project to demonstrate qualification of the alloy across multiple AM machines and sites with the goal of developing requisite SAE PBF feedstock and material specifications with supporting statistical material allowables for future inclusions in the Metallic Materials Properties Development and Standardization (MMPDS) Handbook. Additional data needed for component design and enhanced specifications (including creep, fatigue, and microstructure) are also being generated for proliferation of datasets for future users of the alloy. In working through the existing SAE qualification framework, a number of challenges were addressed to adapt the specification requirements to the unique attributes of the alloy, future applications, and machine variables. The project started by defining key characteristics (KCs) of the alloy and key process variables (KPVs) for AM process evaluation. Pre-qualification work involved a KPV study on a single batch of powder across five machines considering the impact of layer thickness, heat-input, heat-treatment, sample location, and sample orientation. Sensitivity analysis was also performed to assess the impact of minor variations in laser power and solution-heat-treatment temperature. Statistical analysis evaluated these variables against tensile test results and large area microstructural analysis. Initial comparisons to elevated temperature tensile, and creep behavior at 800 °C are also presented and compared to prior work to describe ongoing activities to expand qualification activities to a minimum of 10 heats of powder and 20 manufacturing lots.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.047
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.227
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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