An advantageous imaging perspective for quantitative evaluation of 7075 aluminum alloy grain boundary precipitates using scanning electron microscope
Bibliographic record
Abstract
7075 Aluminum alloy (AA7075) samples undergone four aging sequences were examined using a scanning electron microscope (SEM) and a transmitted electron microscope (TEM). The measurements results validate the correlation between stress corrosion cracking (SCC) resistance and the size and inter-distance of the grain boundary precipitates (GBPs). To evaluate the size and inter-distance of GBPs, we demonstrate in this study a highly efficient SEM imaging technique that can unfold grain boundary in a two-dimensional view. Compared to TEM, imaging with backscattered electrons in SEM (SEM-BSE) is more advantageous for GBPs presentation and measurements. The major reason is that about 900 times more sampling area can be imaged with SEM from the same specimen for TEM observation, thus enabling frequent appearances of GBPs at normal top view perspective, a planar view best for GBPs quantitative analysis but not well-documented. The acceleration tension of SEM for imaging was optimized at 10 kV with an information depth of around 330 nm. RESEARCH HIGHLIGHTS: Scanning electron microscope (SEM) imaging using backscattered electrons is efficient for AA7075 grain boundary precipitate imaging. The precipitate size and inter-distance can be more accurately measured with the perspective of normal top view under SEM than transmitted electron microscope.
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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 | 0.001 | 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) | 0.002 | 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".