Engineering of textured gradient microstructures using directed energy deposition: The impact of adaptive cooling rate
Bibliographic record
Abstract
Textured gradient microstructures can be engineered by tailoring the molten pool cooling rate during additive manufacturing (AM). Here we consider the design of grain structures and crystallographic orientations by controlling the solidification strategy during AM by directed energy deposition (DED). In this paper the textures generated by open loop (fixed scan speed of 100, 200 and 300 mm/min) and closed loop adaptive control to achieve cooling rates of 500, 1100 and 1750 °C/s were compared using electron back scatter diffraction (EBSD). The cooling rate was determined key to eliminating the microstructural gradients or anisotropy for DED parts or to engineer functionality along the desired path. The solidified macro-textures along the building direction were significantly affected by the formation of columnar grains, their growth direction, and morphological transition to equiaxed grains as a result of rapid cooling.
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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 | 0.000 |
| 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".