Hybrid Manufacturing Decomposition Rules and Programming Strategies for Service Parts
Bibliographic record
Abstract
Abstract For service parts, production runs are ‘on demand’, and managing the inventory for components or the tooling is expensive. Additive manufacturing (AM) processes lend themselves to this application as their key strength is the ability to fabricate components with no tooling or fixtures. However, several AM processes require significant post processing to remove support materials as well as generate the required surface finishes and feature tolerances. The main purpose of this research is to determine whether a directed energy deposition (DED) AM solution can be used to manufacture selected components that are presently cast, machined, or forged using hybrid manufacturing build solutions, where machining operations are introduced as required. Select DED AM processes are used to fabricate a near net shape, and either final machining or interspersed machining operations are included. A product-process classification schema is introduced to cluster similar build strategies. This provides the background for the decomposition approaches and the process planning strategies. The build times and material usage are included and component redesign is discussed to facilitate the manufacturing process and optimize the design. This is ongoing research and, in future work, an analysis of the heat maps and the resulting mechanical and physical properties will be evaluated for these components.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".