Look-ahead stress-oriented trajectory planning to improve the strength of fused filament fabricated parts
Bibliographic record
Abstract
Fused filament fabrication (FFF) is a promising additive manufacturing method; nevertheless, the mechanical properties of its final products, particularly for end-use applications, still require enhancements. Combining FFF's low cost and well-established technology with enhanced mechanical properties would increase its competitiveness among other additive manufacturing methods. Similar to the well-established subtractive manufacturing methods, the majority of the trajectory planning algorithms developed for FFF, prioritize print time and dimensional accuracy. However, the effect of trajectory planning on the strength of parts produced through FFF has not received adequate attention. This paper proposes a look-ahead trajectory planning algorithm for FFF, which generates an optimized nozzle path to align the print direction with the principal stress direction. This alignment has been proven effective in increasing the tensile properties of the FFF parts. To minimize print defects, especially when printing high-viscosity materials like metals, path intersection elimination, single point elimination, and linear approximation algorithms are applied to the method. The validity of the proposed algorithm was initially tested on more convenient Polylactic Acid (PLA) specimens and was further verified by experimentations on 17–4PH stainless steel specimens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".