Modeling and optimization of cranial suture anisotropic material properties using a response surface methodology
Bibliographic record
Abstract
The present study aimed to develop and validate a transversely isotropic finite element (FE) model of the cranial suture that predicts suture mechanics, validated using ex-vivo data from the swine internasal suture. A 2D displacement-controlled FE model of the bone-suture-bone complex was constructed using microcomputed tomography ( ) images, with a uniform cross-section and boundary conditions replicating experimental tensile tests. Suture geometry was modeled at three evenly spaced positions to explore how material anisotropy captures regional mechanical variation. Collagen fiber orientation was quantified from histological sections based on fiber angles relative to the suture-bone interface. Transversely isotropic material parameters were identified and optimized to match ex-vivo experimental outcomes using response surface methodology (RSM) with a five-level central composite design. Nodal forces at the displaced bone face were used from FE simulations to compare with experimental force-displacement measurements. Analysis of variance revealed that shear modulus , and Young’s moduli , and significantly influenced force response (p < 0.05). Transitioning from isotropic to transversely isotropic material behavior led to a reduction in strain energy within the suture. Regional variation in suture interdigitation and thickness affected fiber alignment, enabling greater deformation and influencing mechanical behavior. The presented study developed a 2D FE model that incorporated transversely isotropic material properties to better predict the mechanical behavior of the region-specific internasal suture geometry. By incorporating histology-based collagen fiber orientation and optimizing transversely isotropic material properties using experimental data, the model captured region-specific mechanical responses, offering new insight into the structural role of anisotropy in cranial suture mechanics. • Developed 2D suture FE models with region-specific anisotropic properties • Response surface method used to optimize transversely isotropic parameters • Differences in strain observed between isotropic and anisotropic material models • Identified regional variations in response due to fiber orientation changes • Results highlight need for anisotropic suture modeling for local strain predictions
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".