On the Influence of Alloy Composition on the Additive Manufacturability of Ni-Based Superalloys
Bibliographic record
Abstract
Abstract The susceptibility of nickel-based superalloys to processing-induced crack formation during laser powder-bed additive manufacturing is studied. Twelve different alloys—some of existing (heritage) type but also other newly-designed ones—are considered. A strong inter-dependence of alloy composition and processability is demonstrated. Stereological procedures are developed to enable the two dominant defect types found—solidification cracks and solid-state ductility dip cracks—to be distinguished and quantified. Differential scanning calorimetry, creep stress relaxation tests at 1000 °C and measurements of tensile ductility at 800 °C are used to interpret the effects of alloy composition. A model for solid-state cracking is proposed, based on an incapacity to relax the thermal stress arising from constrained differential thermal contraction; its development is supported by experimental measurements using a constrained bar cooling test. A modified solidification cracking criterion is proposed based upon solidification range but including also a contribution from the stress relaxation effect. This work provides fundamental insights into the role of composition on the additive manufacturability of these materials.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".