Acoustic Emission Mechanisms During Polymer Processing and Chain Orientation: From Amorphous to Crystalline
Bibliographic record
Abstract
Acoustic emission (AE) technology has emerged as a highly sensitive and non-destructive method for the real-time monitoring of defect formation and microstructural changes during the manufacturing and early service life of polymeric materials and composites. This review highlights the fundamental principles and applications of AE in detecting crystallization-induced defects, such as cavities, dislocations, and microcracks, as well as plastic deformation mechanisms, including chain orientation, cavitation, and stress release. It is shown that AE activity correlates strongly with crystallinity and processing conditions, providing critical insights into microstructure-property relationships. The possible mechanisms can be the friction between grain boundaries, the local stress release, chain movement, phase changing, and fiber/filler debonding, among others. A comprehensive understanding can help with the prediction/prevention of early defects in the crystalline polymer processing. Furthermore, integrating AE with artificial intelligence and multi-sensor data fusion offers promising pathways toward smart, adaptive manufacturing systems capable of real-time quality control and early defect diagnosis in high-performance polymer composites.
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 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".