A Finite Element Approach to Optimize Fiber Paths of Tow Steered Composites Using Unstructured Mesh Technique
Bibliographic record
Abstract
This study proposes a finite element-based methodology to optimize curvilinear fiber paths in Tow Steered Composites (TSCs) by tuning two governing fiber orientation parameters, T 0 and T 1 .These angles define the spatially varying fiber direction along the lamina, directly influencing structural stiffness and buckling behaviour.MATLAB is employed to extract the complete central fiber path definitions for a flat square plate made up of a single lamina, with parametric sweep of 81 combinations of (T 0 , T 1 ), the sole parameters which govern the central fiber path.For the purpose of finite element modelling, a novel approach of executing unstructured mesh is adopted in ANSYS Workbench, with an objective to cover the no overlap and overlap regions precisely for each of the combinations of (T 0 , T 1 ) in the entire plate, subsequently the combinations are evaluated by first conducting simple static structural analysis under axial compression, followed by linear eigenvalue buckling analysis.Buckling load factors are extracted to identify optimal fiber path configurations.While overlap-related defects from fiber placement are acknowledged, the shift distance optimization is discussed as future work.The findings highlight the critical role of fiber orientation tailoring in maximizing structural performance under compressive loads.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".