Toolpath Generation for Additive Manufacturing Considering Structural Performance
Bibliographic record
Abstract
Abstract The use of additive manufacturing (AM) has increased considerably in recent years. This technology possibilities the creating of complex shapes in an easy, fast, and with low wast of material. This creative freedom allows components to be highly optimized. Currently, some algorithms allow final users to perform topology optimization in the computer-aided design (CAD) phase. However, the optimization results might not be respected or considered during the downstream AM planning processes like slicing hence the optimized structural design may be lost during the actual fabrication process. This work has a focus on topology optimization in the toolpath planning process by taking into account the characteristics in the AM processes. This work develops a line based topology optimization using the principal stress line (PSL) as the guidance in generating optimized toolpaths. The method is efficient, controllable, and able to consider the characteristics of the AM process. Experimental structural tests were performed on the proposed method, and the results obtained demonstrate that the strategy of applying PSL-based optimization in toolpath planning is a promising direction to complement the topologically optimized results from the CAD phase.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".