Simulation of mechanical performance of nanoporous FCC copper under compression with pores mimicking several crystalline arrays
Bibliographic record
Abstract
The mechanical performance of porous metal with assembly of pores mimicking typical crystalline structures is studied via atomistic simulation and finite element method. The pore lattices are made with the same orientation as the face-centered cubic (FCC) copper lattice. The compression is applied in the [0 0 1] direction. Under the same initial porosity and identical pore size, pores assembled in diamond array result in a superior stress response under compression. The sample with pores assembled in body-centered cubic array, whose surface-to-volume ratio is close to that of either FCC or hexagonally close-packed (HCP) array, has a yet much higher yield stress. However, the FCC- and HCP-structured nanoporous samples exhibit a greater hardening effect. The Lubarda model for critical stress to trigger dislocation emission is extended to the nanoporous geometry numerically. The magnitude and distribution of shear stress on the slip plane are found crucial to dislocation activities. No strong correlation between dislocation formation and early densification of nanoporous geometry is found. Through comparing the yielding and hardening behavior among differently structured nanoporous samples, new understanding could be established on their mechanical performance. Enhanced structural integrity could better support their diverse applications by design.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".