Characterization and mesoscale modeling of an enhanced UHMWPE fabric treated with bis-diazirine: multicriteria crosslinker selection and surrogate-based inverse parameter estimation
Bibliographic record
Abstract
Ultra-high molecular weight polyethylene (UHMWPE) woven fabrics are commonly used in armor applications due to their superior biaxial mechanical and physical properties. In this study, three different diazirine-based crosslinker options were initially considered as the chemical treatment applied to a dry UHMWPE plain weave to improve a range of its properties. The optimum crosslinker was then selected using a VICOR multicriteria decision-making model. Specifically, through the bias-extension and yarn pull-out tests, it was observed that the optimum crosslinker significantly enhanced (> 100%) the crossover interactions between the warp/weft yarns. Subsequently, a mesoscale finite element model was developed to predict both the tensile and shear responses of the untreated and treated fabrics. In developing this model, an inverse analysis was employed to capture the effect of yarn transverse elastic modulus and the friction at the crossovers-two properties that are known to be difficult to measure directly in the weave form of yarns. These parameters were sampled using a design of computational experiments and then optimized via a surrogate-based model. Finally, challenges presented by the crosslinking at the single yarn level during characterization are discussed and resolved numerically. For both the treated and untreated fabrics, the mesoscale model is shown to predict the material behavior accurately.
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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 | 0.000 |
| 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 | 0.001 | 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 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".