Effect of Processing Parameters on Cellular Structures and Mechanical Properties of PMMA Microcellular Foams
Bibliographic record
Abstract
In this study, the effect of processing parameters on the cellular morphologies and mechanical properties of PMMA microcellular foams is investigated. Microcellular closed cell Poly(methyl-methacrylate) (PMMA) foams were prepared using a two stage batch process method. The foam structure was controlled by altering the processing parameters such as foaming temperature, foaming time and saturation pressure. The foam morphologies were characterized in terms of the average cell size, cell density and foam density. Elastic modulus, tensile strength and elongation at break were studied as functions of the different foaming parameters. The mechanical properties were found to be greatly affected by the foaming parameters and vary with changing the cell morphologies. The experimental results were compared with existing analytical models to validate them and to predict the mechanical properties of microcellular polymeric PMMA foams prepared with different processing parameters. A constitutive equation for the nonlinear elastic behavior of polymeric microcellular foams was developed based on the Maxwell viscoelastic model. The results of this work can help designers optimize the foam processing parameters and achieve desired foam morphology and mechanical properties.
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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".