Powder particle temperature distribution in laser deposition technologies
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide a flexible tool to predict the particle temperature distribution for traditional laser applications and for the most recent diode laser processes. In the past few years, surface processing and rapid prototyping applications have frequently implemented the use of powder delivery nozzles and high power fibre‐coupled diode lasers with highly convergent laser beams. Owing to the complexity and variety of the process parameters involved in this technology, mathematical models are necessary to understand and predict the deposition behaviour. Modeling the dynamics of the melting pool and the particle temperature distribution is critical for achieving a good deposition quality. Design/methodology/approach This study focuses on the development of mathematical models to predict the particle temperature distribution over the melting pool. An analytical and a numerical solution are proposed for two cases of laser intensity distribution: top hat and Gaussian. Findings The results show that a more vertical position of powder delivery nozzle will lead to a higher and more uniform particle temperature distribution, in particular for the top‐hat intensity distribution case. Originality/value Previous work has dealt only with Gaussian laser spatial distributions and collimated laser beams. Therefore, they were limited to a specific class of laser processes. This work provides a flexible tool to predict the particle temperature distribution for traditional laser applications (powder delivery nozzle and Gaussian laser profile) and for the most recent diode laser processes (powder delivery nozzle and top‐hat laser distribution with highly convergent laser beam). In addition, the results demonstrate that the particle temperature does not monotonically increase while increasing the nozzle inclination as in the case of a collimated laser beam, but some particles show a minimum temperature for intermediate values of the nozzle inclination angle.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".