Jigsaw Pattern Infills Inspired by Toughening Mechanisms of Diabolical Ironclad Beetle for 3D Printing Technologies
Bibliographic record
Abstract
Abstract The Jigsaw puzzle‐like biological shape of the diabolical ironclad beetle, renowned for exceptional toughness due to interlocking and laminated blade geometries, serves as inspiration for advanced infill designs in fused filament fabrication (FFF) 3D printing. Thus, a biomimetic Jigsaw pattern infill is developed to mitigate the structural failure typically observed in conventional infill under mechanical loads. Through experimental and computational analyses, the biomimetic features of the Jigsaw pattern infills have been verified. Additionally, central composite design statistics are conducted to identify comprehensive trends in design and 3D printing parameters. The synergetic effects of the infills with continuous carbon fiber reinforcement (CCFR) in 3D printing have been investigated. The Jigsaw infill minimizes mechanical strength losses caused by variability in infill density and extrusion flow rate, outperforming conventional infills. Moreover, specimens with Jigsaw infills (ASTM D790) achieve a flexural strength of 338.9 MPa at a carbon fiber volume fraction of 23.58%, surpassing that of specimens with line‐based solid infill (305.5 MPa at 32.16%). Jigsaw infills with CCFR have been intuitively demonstrated through 3D printed carjack applications, which reliably endure loads of up to 1089 kg. These features highlight the potential for lightweight structural applications in the automotive and robotic industries.
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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 | 0.000 | 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".