Fatigue crack initiation and propagation in polyamide‐6 and in polyamide‐6 nanocomposites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it