Application of picosecond laser for polishing of AISI H13 tool steel sample prepared by micro milling
Bibliographic record
Abstract
This paper explores the applicability of picosecond laser (ps) in laser micro polishing (LμP) of AISI H13 tool steel. The melting regime associated with this process was determined experimentally through the variation of the focal offset in order to attain a desired fluence level. To ensure the consistency and certainty of initial surface geometry, the surface was prepared through a micro milling operation with a step-over of 50 μm and scallop height of 2 μm. The LμP experiments were performed at 5 different fluence levels of the melting regime obtained through setting of the focal point at 5 different distances. The polishing performance was evaluated based on the distribution of line profiling average surface roughness (Ra) at various spatial wavelength intervals. Additional statistical metrics such as material ratio function and power spectral density function were also determined in order to establish the process parameters associated with best achievable surface finish. Finally, as a demonstration of the applicability of ps LμP process, a flat micro milled area was polished using optimum process parameters. This operation yielded in 68% surface quality improvement as quantified through the reduction of the areal topography surface roughness (Sa) from 0.53 urn to 0.17 μm.
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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 | 0.000 | 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) | 0.001 | 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".