Fatigue crack initiation and propagation in polyamide‐6 and in polyamide‐6 nanocomposites
Bibliographic record
Abstract
Abstract Recent developments in polymer nanocomposites have led to improvements in conventional short‐term, but the long‐term mechanical properties have received little attention. The objective of the present study was to characterize the effect of nanoparticles on the fatigue crack initiation and propagation mechanisms and on the fatigue properties of polyamide‐6 (PA6) nanocomposite (PA6NC) prepared by in situ polymerization with montmorillonite clay. Two approaches were employed: fatigue life measurements and crack growth monitoring. Compared with non‐filled PA6 at the same stress amplitude, the number of cycles to fracture was higher for the nanocomposite, which suggests an increase in the intrinsic resistance of the material to crack initiation. However, the crack growth rate results indicated that nanoparticles decreased the resistance to crack propagation. Post‐fatigue fractographic observations indicated a change in the fatigue crack propagation mechanism resulting from the addition of nanoparticles, primarily attributed to the increase in yield stress, which favors the development of a fibrillated deformation zone. The fibrillation process in the relatively high crack propagation rate regime appeared to be preceded by plastic deformation at approximately constant volume. Most of the effect of nanoparticles on the fatigue behavior and properties results probably from the mechanical reinforcement on the microstructure and its effect on the yield stress and Young's modulus rather than from the effect of the inorganic filler to act as a stress concentrator. Polym. Compos. 25:433–441, 2004. © 2004 Society of Plastics Engineers.
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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 | 0.000 | 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) | 0.001 | 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".