Process planning solution strategies for fabrication of thin-wall domes using directed energy deposition
Bibliographic record
Abstract
Although multi-axis bead deposition-based additive manufacturing processes have been investigated in many aspects in the literature, a general process planning approach to address collision detection and prevention still needs to be developed to fabricate complex thin-wall geometries in a supportless fashion. In this research, an algorithm is presented that partitions the surfaces of the part and finds the appropriate tool orientation for each partition to avoid collisions.1 This algorithm is applied to segment the surface of a thin-wall hemisphere dome and fabricate it without the need of support structures. Two main fabrication strategies are developed: wedge-shaped partitioning, and a rotary toolpath. A five-axis toolpath and a 2 + 1 + 1-axis toolpath is introduced to fabricate the partitioned build scenarios. A rotary (1 + 3-axis) toolpath is also developed. Tool paths are developed, and the domes built using a directed energy deposition process. The built geometry aligns well with the process planning solutions, but material build up is observed at the partition interfaces. Planar slicing is used to generate toolpaths. However, it is concluded that planar slicing brings limitations to reduce the number of partitions that can be modified by a constant-step-over toolpath.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".