Direct manufacturing of net-shape functional components/test-pieces for aerospace, automotive, and other applications
Bibliographic record
Abstract
Laser consolidation (LC) is a novel computer-aided manufacturing process being developed by the Industrial Materials Institute of National Research Council of Canada (NRC-IMI). This rapid manufacturing process produces net-shape functional metallic parts layer-by-layer directly from a computer-aided design (CAD) model by using a laser beam to melt the injected powder and resolidifying it on the substrate or previous layer. As an alternative to the conventional machining process, this novel manufacturing process builds net-shape functional parts or features on an existing part by adding instead of removing materials. In this paper, laser consolidation of various high performance materials (such as Ni-alloys, tool steels, etc.) will be demonstrated to manufacture functional components or test-pieces (such as shell-based mold inserts, impeller, intersected multihexagon tube, goblet shape, etc.) for potential aerospace, automotive, and other applications. In the paper, dimensional accuracy of various laser-consolidated test-pieces will be measured and compared with CAD models. In addition, an example will be given on laser consolidation to build complex cold spray nozzles that are difficult to make otherwise and significantly improve the performance.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".