Application of Additive Technologies in the Production of Aircraft Engine Parts
Bibliographic record
Abstract
The use of rapid prototyping technologies provides a unique opportunity of cost-effective methods of investment casting to create new industrial products, of particular importance is the cost and speed of production. Development and research of rapid prototyping technologies have allowed a new level of optimization and introduction of new technologies into various industries. The essence of investment casting is that to get the castings use a single, accurate non-split, ceramic shell molds, which are produced on single models using liquid molding compounds. Before pouring the melt, a model shape is destroyed by melting, burning, dissolving, or evaporation. To remove residues of the model and hardening, mold is heated to high temperatures. Calcination form before its filling virtually eliminates gas formation and improves occupancy melt. At the stage of pilot production, which is characterized by frequent changes in design, the problem of the rapid production of cast components becomes crucial. This is mainly due to the complexity of manufacturing foundry equipment. The aim of this work is to identify opportunities and evaluate accuracy of the casting size during investment casting using rapid prototyping technology. The work was conducted with the use of cross-cutting design in CAM / CAD / CAE systems. The work has been verified according to the adequacy of the virtual simulation of the casting formation process in the casting simulation ProCAST, in comparison with those obtained castings. The study showed that the use of rapid prototyping technologies with investment casting can significantly reduce the time for making castings, reduce production costs and improve the accuracy of the casting size.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".