Learnings in the Qualification of ABD®900AM for Turbine, Aerospace, and Energy Applications
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".