Application of Additive Technologies in the Production of Aircraft Engine Parts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it