Designing UV-Curable Resin-Made Polymeric Foams with Lattice Structures for Desired Stiffness via Machine Learning
Why this work is in the frame
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Bibliographic record
Abstract
Several industries extensively utilize polymeric foams due to their exceptional characteristics. The mechanical properties of a foam structure play a significant role in compression, so it is necessary to optimize foam deformation to achieve the desired outcome. The cell structures of foams are created randomly, but the issue has been resolved by lattice structures. Compared with traditional foams, lattice structures can enhance mechanical properties and facilitate the development of novel applications. Despite extensive research on lattice structures in both rigid and soft materials, there is a notable lack of predictive modeling specifically for soft thermoset Ultraviolet (UV)-curable lattice structures. This study employs additive manufacturing (AM) and machine learning (ML) to address this discrepancy. In this work, 93 lattice designs were produced using AM and evaluated for their geometric structure through compression tests utilizing ML techniques, specifically artificial neural network (ANN) and random forest (RF). The process involves the preparation of data, training of ML models, and evaluation. The RF model surpasses ANN model and is the most effective at predicting lattice geometries using force, strain, and lattice-type inputs in a graphical user interface. Hence, this study improves ML comprehension and utilization in the design of lattice structures to optimize the performance of soft materials across diverse applications.
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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 | 0.001 | 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