Bibliographic record
Abstract
Fused Deposition Modeling (FDM) is the most widely used additive manufacturing technique, but its reliance on a single principal build direction introduces anisotropic material properties, limited surface quality, and strict geometric constraints requiring supports. Multi-axis FDM offers a pathway to overcome these limitations, yet existing solutions demand complex, costly hardware and bespoke slicing software. Here, we present a pragmatic approach to multi-axis fabrication that leverages standard 3-axis printers and conventional slicing workflows through the integration of in-situ printed work-holding components. This method enables sequential reorientation of parts within the printer’s coordinate system, allowing fabrication across multiple build directions without specialized equipment. We demonstrate the technique through case studies including a multi-axis cube and a turbine blade, achieving reduced support requirements, improved mechanical interfaces, and tolerances within 100–300 µm depending on geometry and holding configuration. Furthermore, we evaluate multi-material adhesion across common thermoplastics, identifying strong candidates for multi-axis, multi-material constructs. This accessible framework broadens the design space of FDM and lowers the barrier to advanced geometries, offering a versatile tool for both research and practical applications.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".