The Effect of Processing Parameters on the Acoustical Efficiency of Open-Cell PMMA Materials
Bibliographic record
Abstract
Porous materials are widely used for noise control in the automotive industry. However, due to the weight and space saving considerations, the use of bulky thicknesses of these materials is limited in industrial applications, thus reducing the effectiveness of the acoustic treatment. Consequently, thinner materials with improved acoustic properties are required to achieve a satisfactory noise reduction. One of the promising methods to enhance the acoustic performance of a porous material is the optimization of its macroscopic acoustic parameters through inner micro-structure properties control. Thematically, it is of interest to understand and link material inner micro-structure such as cell size, cell distribution, etc. to main macroscopic parameters which control the sound absorption of a material such as porosity, flow resistance, etc. This study presents a new processing technology to manufacture open-cell PMMA materials using a gas foaming/particulate leaching method and a constrained foam molding process. A parametric study is conducted by altering the processing parameters such as foaming temperature, size and percentage of foaming agent which affect the cell morphologies and control the macroscopic properties. The acoustic performance is controlled by adjusting the processing parameters with respect to the inner structure of the material. The correlations between the resulting cell morphologies and sound absorption effectiveness are investigated. The results conclude that understanding and controlling the porous material inner structure through adjusting the processing parameters are crucial for the development of porous materials with optimized acoustical efficiency.
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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 | 0.001 |
| 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".