A Characterization of Laser Cleaning Painting Layer From Steel Surface Based on Thermodynamic Model
Bibliographic record
Abstract
Abstract In this study, the environmentally friendly nanosecond ultraviolet (UV) laser is innovatively employed laser cleaning to remove the painting layer from the AH36 steel substrate. The feasibility of UV laser cleaning the painting layer is innovatively proposed and it has been calculated by the model theoretically, followed by elaborating the prominent interaction mechanism of UV laser exactly. The initial cleaning threshold and completely cleaning threshold are 2 J/cm 2 and 5 J/cm 2 , respectively. Afterwards, the UV laser cleaned surface quality is evaluated by the scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), optical microscopy (OM) and optical profiler (OP), respectively. The mechanical properties have enhanced dramatically after laser cleaning and characterized by the Vickers hardness tester and universal testing machine. By varying laser fluences (2, 5, 7 J/cm 2 ) during laser cleaning, microstructures registering various sizes of corrugated shaped, craters and ring-shaped could be acquired. In addition, the mechanical properties analysis including rapid melting, quenching and dislocation density effects illustrates that laser cleaning could effectively increase surface microhardness, tensile strength and bending strength. Thus, laser cleaning method has emerged as a favourable means to strip painting layer in lieu of traditional methods for marine industry as well as this study could promote the development of laser cleaning in the field of marine engineering.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".