A phase field methodology for simulating the microstructure evolution during laser powder bed fusion in-situ alloying process
Bibliographic record
Abstract
Abstract A recently-developed [1] multi-component phase field model has been utilized to investigate microstructure evolution during in-situ alloying of a blended elemental Ti-1Al-8V-5Fe alloy powder via the Laser Powder Bed Fusion process. The process of in-situ alloying, where elemental powder is used instead of pre-alloyed powder, was studied by performing two simulations having: (1) a uniform initial composition, and (2) a spatially varying initial composition to represent different powder particles. Specifically, the grain morphology, solute distribution, competitive growth and nucleation under the two different scenarios were simulated and compared. To assist the microstructure simulations, a macro-scale finite element model was developed to simulate the heat transfer during LPBF process. The thermal history data calculated by the finite element model was provided to the phase field model in order to simulate transient dendritic growth behaviour. The results show that a set of evenly-spaced columnar dendrites form in the uniform initial composition case, whereas when the initial composition is spatially varying, non-uniform dendrites having elongated shape can develop. It is also shown that competitive growth between dendrites is influenced by nucleation. For the spatially varying initial composition case, the results indicate that full alloying is difficult to achieve during the LPBF printing process; this incomplete alloying greatly influences the dendrite morphology and solute distribution.
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 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".