Real-time prediction of dilution for automated laser powder deposition process
Bibliographic record
Abstract
This paper presents a mathematical model for the real-time monitoring of the dilution in the laser powder deposition process. The proposed model 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. The model is based on the solution of heat diffusion equation using the solution of the heat diffusion due to a point heat source and the superposition principle. Numerical and experimental analyses show a non-linear behavior of the melt pool depth as a function of scanning speed when the other process parameters are kept constant. Using the model that has been validated by experiments, a combined parameter is introduced. This combined parameter which is a nonlinear function of the laser power, scanning speed and the clad height, has the most correlation with the melt pool depth. The comparisons between the numerical and experimental results show that this model is capable of predicting the characteristics of the laser powder deposition process accurately.
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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".