Geometry-Based Decomposition Rules and Programming Strategies for Complex Components in Additive and Hybrid Manufacturing
Bibliographic record
Abstract
Abstract Additive manufacturing (AM) processes offer a promising avenue for providing service components, primarily due to their inherent advantage of producing components without the need for tooling or fixtures. Nevertheless, many AM processes often necessitate extensive post-processing steps to eliminate support materials and achieve the required surface finishes and feature tolerances. The central objective of this research is to investigate the feasibility of using directed energy deposition (DED) AM solutions to manufacture intricated geometries that are traditionally produced through casting, machining, or forging, leveraging hybrid manufacturing build techniques where machining operations are introduced as needed. DED AM processes with innovative tool paths and build strategies are employed to create a near-net shape, followed by final machining or intermittent machining operations. To structure our approach, we introduce a geometry classification schema, which allows us to group similar build strategies. This classification framework lays the foundation for our decomposition methods and process planning strategies. Some issues, such as overhang geometries and collisions, have been resolved using these specific strategies. It is important to note that this research is ongoing, and in future work, we plan to develop in-line heat maps and explore heating cycles impact on the resulting mechanical, tribological and physical properties of these components. This continued exploration will further enhance our understanding of the potential of DED AM in this context.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".