Prediction of melt pool depth and dilution in laser powder deposition
Bibliographic record
Abstract
This paper presents a mathematical model of laser powder deposition (LPD) to predict temperature field, melt pool depth and dilution. The model validated by experiments is developed using the moving heat source method. In this method, the temperature distribution inside the clad and the substrate is obtained using the superposition principle and the solution of the heat diffusion due to a point heat source. The model, which can be used in real-time applications, predicts the melt pool depth and dilution as a function of clad height and clad width, which in practice can be measured by a vision system. Numerical and experimental analyses show a non-linear behaviour of the melt pool depth as a function of process speed. This indicates that the melt pool depth has a maximum at a certain process speed. The comparisons between the numerical and experimental results show that this model is capable of predicting the characteristics of the LPD process accurately. Using the model, some general curves that show the behaviours of the melt pool depth and dilution as a function of clad height, scanning speed and laser power are illustrated.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".