Imprinting of Micro-/Nano-Textures onto Metals and Alloys with Use of the Laser-Printed DLC-Die
Bibliographic record
Abstract
This paper focused a two-step procedure to imprint the tailored emblems, patterns, symbols and codes onto the metallic and polymer product surfaces. The laser printing was first used to form these tailored micro-/nano-textures onto a Diamond-Like Carbon (DLC) coating die. The DLC film with the thickness of 20 mm and the hardness of 22 GPa was utilized as a mother die. Femtosecond laser printing was used to shape the tailored micro-/nano-textures on this die. Seven emblems such as a star-patterned texture with the maximum depth of 4 mm were just cut into the DLC-die to have color-grating by micro-texturing and surface plasmonic brilliance by nanotexturing. In second, Computer Numerical Control (CNC) – stamping was used to imprint these textures onto the aluminum alloy plate with the thickness of 1 mm. Scanning Electron Microscopy (SEM) and three dimensional profilometer were used to investigate the geometric accuracy in this two-step printing procedure. The constituent micro-/nano-textures of each emblem was accurately imprinted onto the aluminum work. The optical properties were also duplicated together with this geometric imprinting.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".