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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".