Analysis and Comparison of Techniques for Artificially Preparing Typical Aero-Engine Disk Material Defects
Bibliographic record
Abstract
Material defects in titanium alloys, powder alloys, and wrought superalloys can compromise the structural integrity of aero-engine disks, thereby leading to uncontained failure, which poses a significant threat to aircraft safety. Studying how material defects affect rotor disks provides technical support for improving aircraft safety. Since natural defects rarely occur during normal production, researchers have developed methods to prepare defect-containing materials artificially. These methods primarily utilize hot isostatic pressing (HIP) to bond the defects into materials. Artificial defects are now well-established for titanium alloys and powder metallurgy alloys and are widely used in research. This paper describes the existing methods for creating artificial defects in titanium alloys and powder metallurgy alloys. Besides, this paper presents experiments creating "dirty white spot" defects in GH4169 superalloy. The experimental results show that although GH4169 with dirty white spot defects prepared by HIP can meet the requirements of ultrasonic testing research, the low mechanical strength of the HIP interface will affect the fatigue test results. Then, the differences and causes of the three types of materials containing artificial defects were compared and analyzed.
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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 | 0.002 |
| 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 | 0.000 | 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".