Minimizing Voids With Using an Optimal Raster Orientation and Bead Width for a Material Extrusion Based Process
Bibliographic record
Abstract
Additive Manufacturing (AM) is the process of joining materials ‘layer by layer’ to make products from Computer Aided Design (CAD) model data. AM processes support faster product realization for a wide selection in industries. The Material Extrusion (ME) process is an AM process that builds a product from thin layers of extruded filaments from a semi-melted material such as a thermoplastic. In commercial systems, the software automatically generates the tool paths for both the model and any necessary supports, based on the curve geometry and the specified build parameters. The interior fill rotates 90° between each layer. Automatically generating the tool path can be the biggest weakness for this process planning strategy. Voids and discontinuities have been observed after evaluating test specimens developed to explore mechanical characteristics. Choosing an optimal raster orientation and bead width will help minimize voids and discontinuities in each layer. A mathematical model is introduced in this paper to find optimal raster orientation and bead widths based on the geometry of the slice for selected 2D extruded parts. As well, preliminary quality assessment metrics are introduced. Void analysis is performed to evaluate solution approaches, and the results compared. The future work will investigate utilizing multiple bead widths for a layer to minimize voids, and developing more comprehensive quality metrics to highlight problematic regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".