Geometric Misorientation Changes in Aluminium Subjected to Strain Path Change Test
Bibliographic record
Abstract
Specimens of commercial purity aluminum were subjected to a strain path change test during high temperature deformation. Specimens were deformed at 4000 C and strain rate of 0.1 s-1 up to various strains of 0.2, 0.5, and 1. Then in a strain path change test, specimens were first deformed to a strain of 0.5, and subsequently deformed to strains of 0.2 and 0. In order to further the understanding of the deformation mechanisms in aluminum, the subgrain sizes and misorientations were characterized in detail by comparative studies using optical microscopy in polarized light (POM), orientation imaging microscopy (OIM/SEM) and transmission electron microscopy (TEM). The analysis revealed that while subgrain size is relatively insensitive to strain, overall misorientations increased with increasing strain. These analyses confirmed a strong bimodal distribution of boundaries during deformation coupled with a low fraction of medium angle boundaries. The results contribute to the understanding that dynamic recovery in aluminum maintains subboundaries with low misorientation but as grains elongate and more subgrain become adjacent to grain boundaries the fraction of high angle boundaries rises.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".