Experimental statistical analysis of laser micropolishing process
Why this work is in the frame
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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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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) | 0.001 | 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 it