Experimental statistical analysis of laser micropolishing process
Bibliographic record
Abstract
Laser micropolishing (LμP) is a new advanced material microprocessing technology that attempts to smooth the original surface geometry through laser-material interactions such as melting or material ablation. Despite the significant advantages of LμP micro features, surfaces, parts, moulds and dies with complex 3D geometries from a wide range of materials, LμP is a complicated dynamic process that requires very fine tuning of a number of process parameters related to laser, optics, laser beam motions, and material properties. This paper describes a new approach for statistical analysis of LμP, where LμP is considered as a single-input (original surface) / single-output (polished surface) dynamic system. Original and polished cross-sections were obtained experimentally and their statistical characteristics, such as, surface roughness, material ratio function and autospectrums were calculated and analysed. In addition, LμP process was experimentally investigated as a dynamic operator represented by a transfer function and it was analysed using a coherence function. Analysis of these characteristics allowed finding specific characteristics of the LμP process when surface roughness was improved by 21.3 %, lowering averaged Ra value from 577 nm to 452 nm, and significantly reducing Ra non-uniformity from 132 nm to 44 nm for a Ti6Al4V sample.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".