Engineering of textured gradient microstructures using directed energy deposition: The impact of adaptive cooling rate
Bibliographic record
Abstract
• The development of textured gradient microstructures upon DED building of 316L stainless steel was investigated. • Large-scale EBSD mapping revealed the gradient in grains morphology and orientation preference in multi-layers’ deposition. • Epitaxial growth of grains during DED was engineered using adaptive controlling of the cooling rate between layers. • Solidification texture and mechanism of columnar dendrites formation affected by the severity of adaptive cooling. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".