Modelling and statistical analysis of the intermediate laser remelting regime by moving average filtering
Bibliographic record
Abstract
Surface polishing by laser remelting (SP-LRM) is a novel surface finishing technology that is characterized by rapidity, versatility, repeatability as well as the lack of material removal. SP-LRM can be implemented in several thermodynamic regimes whose presence depends on a complex combination of power-speed-focus-trajectory-material parameters. The intermediate LRM regime exhibits the most desirable surface topography smoothening along with predictability, controllability, and minimum bulging. The experimental observations of the intermediate LRM process have demonstrated the reliable applicability of a moving average filter for modeling and statistical analysis of the surface formation. The obtained results have enabled the identification and classification of the LRM process regimes (shallow-LRM, intermediate-LRM, and deep-LRM) through the optimization of a filter window corresponding to a maximum correlation coefficient found between actual and modelled remelted surface profiles along the LRM track. The proposed statistical modelling of the LRM-based surface formation opens up new research directions for model-based self-optimization and self-control of the SP-LRM process.
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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) | 0.001 | 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 | 0.001 |
| 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".