Laser 3D prototypes forming from metal and ceramic materials
Bibliographic record
Abstract
The possibility to manufacture the experimental models and ready articles from metal and composites, is a result of laser technolo gy (sintering or cladding) and information technology mutual integration [1]. Naturally, at laser Rapid Prototyping* technique development the different schemes of processing (like in a basic technology) may be utilized: melting (sintering) of a preformed layer of a powdered material [2] or its immediate feeding into a zone of laser interaction with material [3]. Each of the schemes has the advantages and lacks. So the advantage of the first method is the possibility of sintering of the ultra disperse powdered material in a solid phase, and the lack appears at forming the “hollow” details, as the powder fills the whole volume and it is necessary to provide the technological windows for its removal. This disadvantage is absent at the second method of processing – the amount of the powdered material which occurred inside the hollow details is minimal. Common for all systems of Rapid Prototyping is: High level of mutual integration;Preparation of a drawings of the designed article with the help of CAD system;Manufacture of an article via “layer by layer” technique;Dependence of outcome of processing on conditions of irradiation, properties of a powdered material etc. This paper is devoted to a problem of technology and equipment for Rapid Prototyping development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".