Investigation of the Effect of Magnification, Accelerating Voltage, and Working Distance on the 3D Digital Reconstruction Techniques
Bibliographic record
Abstract
In this study, the effect of Scanning Electron Microscopy (SEM) parameters such as magnification ( M ), accelerating voltage ( V ), and working distance (WD) on the 3D digital reconstruction technique, as the first step of the quantitative characterization of fracture surfaces with SEM, was investigated. The 2D images were taken via a 4-Quadrant Backscattered Electron (4Q-BSE) detector. In this study, spherical particles of Ti-6Al-4V (15-45 μm) deposited on the silicon substrate were used. It was observed that the working distance has a significant influence on the 3D digital rebuilding method via SEM images. The results showed that the best range of the working distance for our system is 9 to 10 mm. It was shown that by increasing the magnification to 1000x, the 3D digital reconstruction results improved. However, there was no significant improvement by increasing the magnification beyond 1000x. In addition, results demonstrated that the lower the accelerating voltage, the higher the precision of the 3D reconstruction technique, as long as there are clean backscattered signals. The optimal condition was achieved when magnification, accelerating voltage, and working distance were chosen as 1000x, 3 kV, and 9 mm, respectively.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".