Development of an ultrasonic fatigue testing system for gigacycle fatigue
Bibliographic record
Abstract
High cycle fatigue (HCF) in the range of 106 to 108 cycles and very high cycle fatigue (VHCF) in the range of 108 to 1010 cycles are key design criteria for aerospace, automotive, military, transportation, and other industries. However, data gathering in the HCF and VHCF ranges is inefficient with traditional 50- to 100-Hz servo-hydraulic testing machines. The development of high power piezoceramic actuators makes it possible to reliably conduct HCF and VHCF tests within a very short time frame at high frequency on the basis of the ultrasonic fatigue testing approach. An ultrasonic fatigue test machine operating in an axial mode shape of test specimens at 20 kHz was designed and built to investigate VHCF characterization of lightweight metal alloys. The experimental setup went through several stages of design and validation. Finite element analysis was employed to design three versions of an acoustical horn, a single half wavelength booster, and three types of nonferromagnetic samples. The developed ultrasonic fatigue machine was used to conduct fatigue testing of specimens made from 2024-T351 and 7075-T6 aluminum alloys to generate representative HCF and VHCF data. HCF and VHCF data for these alloys were found to be in good agreement with experimental fatigue data from the literature.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".