Machine learning predictions and benchmarking of non-linear mechanical behavior of polymer composites
Bibliographic record
Abstract
The rise of environmental concerns and the worldwide transition to a circular economy is partly fueling the discovery of new materials with unique functionalities that satisfy industry requirements. Polymer composites are among the most popular materials in various metal-replacement applications, such as auto and aerospace light-weighting, electronics, optics and energy storage devices. Nonetheless, their vast compositional design freedom and sensitivity to ambient conditions require extensive physical experiments. To address this issue, the aim of this research is to develop machine learning (ML) algorithms which are able to characterize entire stress-strain curves of polymer composites based on their composition, processing and environmental conditions. Three distinct feature variables including temperature, filler content and strain were utilized to predict the output stress amounts. The results indicated that the artificial neural network (ANN) models, with a new train-test splitting strategy, accurately fit the training data, as evidenced by the root mean squared error (RMSE) values below 3 MPa for PET composites and below 1 MPa for PC composites. Furthermore, the developed ANN models revealed a satisfactory performance on the testing data, with RMSE of less than 1.5 MPa for PC composites and approximately 6 MPa for PET composites. These findings pointed out the effectiveness of ANN models in predicting complete stress-strain curves of polymer composites, especially when a sufficient amount of data is available. The outcomes obtained will pave the way for automated design and characterization of advanced multifunctional composites while minimizing extensive physical testing, thereby advancing the vision set out by Industry 4.0.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".