Polylactic Acid (PLA) Foaming: Design of Experiments for Cell Size Control
Bibliographic record
Abstract
In this study, a design of experiments (DoE) approach was used to develop a PLA open-cell foam morphology using the compression molding technique. The effect of three molding parameters (foaming time, mold opening temperature, and weight concentration of the ADA blowing agent) on the cellular structure was investigated. A regression equation relating the average cell size to the above three processing parameters was developed from the DoE and the analysis of variance (ANOVA) was used to find the best dimensional fitting parameters based on the experimental data. With the help of the DoE technique, we were able to develop various foam morphologies having different average cell size distribution levels, which is important in the development of open-cell PLA scaffolds for bone regeneration for which the control of cell morphology is crucial for osteoblasts proliferation. For example, at a constant ADA weight concentration of 5.95 wt%, we were able to develop a narrow average cell size distribution ranging between 275 and 300 μm by varying the mold opening temperature between 106°C and 112°C, while maintaining the foaming time constant at 8 min, or by varying the mold foaming time between 6 and 11 min and maintaining the mold opening temperature at 109°C.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".