Mechanical and acoustic performance of compression‐molded open‐cell polypropylene foams
Bibliographic record
Abstract
Abstract Open‐cell materials are lightweight and multifunctional capable of absorbing acoustic energy and supporting mechanical load. The acoustic and mechanical performance of open‐cell materials can be optimized through processing. In this article, the relationships between processing parameters and acoustic and mechanical performance are shown for polypropylene (PP) foams. PP foam samples are fabricated using a combined compression molding and particulate leaching process. The results from a parametric study showed that both salt size and salt to polymer ratio affect the acoustic and mechanical performance of open‐cell PP foams. As salt size increases, cell size increased and cell density decreased. The salt to polymer ratio had opposite affect on cell density, and increasing the salt to polymer mass ratio increased the open‐cell content. The airflow resistivity decreased significantly by increasing the cell size, which means that foam samples with smaller cell size have better sound absorption. When foam samples were thin, smaller cell sizes produced better sound absorption; however, as thickness of the sample increases, medium cell size offered the best acoustic performance. The compressive strength of the foams was increased by increasing the relative density. Acoustic performance results from the parametric study were compared to the Johnson‐Allard model with good agreement. Finally, optimal cellular morphologies for acoustic absorption and mechanical performance were identified. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2010
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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 | 0.000 |
| 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".