Utilizing Additive Manufacturing / 3-D Printing to Optimize Design and Support Solutions for One-Off Spares and Support Product Requirements
Bibliographic record
Abstract
Out-of-production aircraft continue to have demand for spare parts that are designed and fabricated with the tooling, processes and materials that were optimized during the high-rate production periods. Similarly, component repair and overhaul support equipment can require broaching, machining, electrical discharge machining (EDM), grinding and polishing and other techniques necessary to achieve reliable functionality of the system. In both cases the low production volume for these parts requires significant non-recurring set-up, tooling, and quality controls costs that affect the per-unit costs and lead times. The maturing technology of additive manufacturing and 3-D printing is now allowing companies to strategize around "growing parts" from a digital database and bypass the design paradigms and production costs inherited from historical manufacturing limitations. Engineers who understand the design freedom of additive manufacturing could leverage the capability and optimize support equipment functionality even further to increase maintainability and safety of usage. As additive materials continue to develop, more and more low-volume spare parts could be converted from traditional, production-driven designs to parts grown-when-needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".