Optimization and prediction of additively manufactured PLA-PHA biodegradable polymer blend using TOPSIS and GA-ANN
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent years have seen the proliferation of fused deposition modeling (FDM) as a means of manufacturing biodegradable products, for different applications such as rigid packaging, agricultural and biomedical. Blends of Polyhydroxyalkanoates (PHA) and polylactic acid (PLA) have been investigated to ascertain their prospective applications through FDM. This paper includes three parameters that affect the build process: layer height, nozzle temperature, and flow rate. 3D printed PLA/PHA can be characterized mechanically, and process parameters can be optimized to maximize design functionality. The experimental setup utilized a Taguchi L9 design, and TOSPIS was employed to optimize the output results. Using TOPSIS analysis, 0.2 mm layer thickness, 195 °C nozzle temperature, and 100 % flow rate were found to be the most optimal initiation parameters. The Taguchi analysis was used to analyze the output responses, and it was found that layer height had the greatest influence on mechanical properties, followed by flow rate and nozzle temperature. The percentage elongation at break has been improved significantly by adding PHA i.e., 170 % compared to PLA (5–10 %). This paper presents a framework for in-depth mechanical characterization of PLA-PHA 3D-printed parts, along with methods for optimizing process parameters to achieve optimal performance, as well as tools for modeling output responses using GA-ANN with an accuracy of 95 %.
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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 it