Modelling Mechanical and Thermomechanical Behavior of Additively Manufactured Quasi-Woven Shells
Bibliographic record
Abstract
Additive manufacturing of continuous carbon fiber-reinforced thermoplastics using the fused filament fabrication (FFF) method presents new opportunities for optimizing the properties of thin shell structures, such as antenna reflectors and deployable booms for spacecraft. These applications frequently employ plain-woven biaxial and triaxial reinforcement architectures due to their quasi-isotropic electromagnetic radiation reflectance properties and minimal coupling between in-plane and out-of-plane deformations. Although true woven architectures can not be produced using the FFF process, as this would require that tows be deposited both above and below previously-deposited ones, quasi-woven architectures can be realized by staggering the tow deposition sequence. The freedom to manipulate the paths, spacings, and deposition orders of tows with the FFF process unlocks an extended design space that can be used to tailor behavioral characteristics of thin shells, such as thermal dimensional stability and electromagnetic radiation reflectivity. However, robust methodologies to characterize their behavioral characteristics need to be developed. Here, automated computational procedures for generating geometrically-accurate finite element models of quasi-woven shells directly from FFF deposition path data are presented. A crimping algorithm is devised for predicting out-of-plane fiber distortions that occur at and between tow intersections, accounting for additional compaction caused by a discrete in-situ consolidation (DISC) process used to improve interlaminar bond strength. The predicted morphology of out-of-plane distortions is validated against optical cross-sectional micrographs of manufactured quasi-woven samples, demonstrating strong agreement. Crimped fiber paths are incorporated into shell element models for each individual tow, with beam elements used to emulate the interlaminar bonds between overlapping tows. Model accuracy is assessed by comparing predicted and observed responses of biaxial and triaxial quasi-woven architectures to tensile and thermal loading. Tensile behaviors show good agreement with experimental results, with predicted stiffness values exhibiting an average error of only 12\%. Unfortunately, measured thermal deformations were found to be entirely uncorrelated with predicted values, likely due to unaccounted for process-induced warpage in the samples.
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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 | 0.001 | 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".