Developing strategies for medium volume production in directed energy deposition additive manufacturing
Bibliographic record
Abstract
Additive Manufacturing (AM) processes enable the validation of design variants, and the manufacturing of low volume specialty components. Slow fabrication times are an issue for larger production volumes, but for the directed energy deposition (DED) and hybrid manufacturing (where additive and machining operations are interwoven), new process planning scenarios can be explored for both low and medium volume production levels, which aligns well with addressing on-demand service and out of production components. DED AM is a material deposition based process. Wire filament or powder is melted by a heat source, and multi-axis tool paths can be employed to deposit the material. Large freeform components can be fabricated without support material; however, production volume scalability is an issue. Prior to exploring multi-function or reconfigurable machines and dynamic layouts, a framework for defining nomenclature for DED AM precedence diagrams and value stream maps, and insights for systematically decomposing components for macro and micro level process planning needs to be developed. The goal of this research is to provide a foundation for DED and hybrid manufacturing for low volume production (100 – 2000 pcs) for short planning horizons (1 week to 1 month) which would align to ‘medium volume’ production levels. This specific paper will present research performed to date on addressing these challenges.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".