Design and Development of an Ultrasonic Fatigue Testing System for Very High Cycle Fatigue
Bibliographic record
Abstract
There has been growing demand for increased fuel efficiency, reduced emissions and improved power performance while maintaining reliability and durability of mechanical and structural systems in many different industries. The structural engineering components often experience long loading histories, typically ten million cycles or greater, i.e. high cycle fatigue (HCF) and very high cycle fatigue (VHCF) regimes. HCF in the range of 106-108 cycles and VHCF in the range of 108-1010 cycles are key design criteria for aerospace, automotive, military, transportation and many other industries. However, fatigue characterization of metal alloys in the HCF and VHCF regimes is hindered by limitations of traditional fatigue testing machines due to time and cost constraints. The development of high power piezoceramic actuators enables efficient and reliable fatigue tests in the HCF and VHCF regimes within a very short time frame on the basis of ultrasonic fatigue testing approaches. A fully instrumented ultrasonic fatigue test machine operating at 20 kHz was designed and built to investigate HCF and VHCF behavior of lightweight metallic alloys. The fatigue testing machine went through several stages of design and development. Finite element modeling was employed to design and optimize three versions of an acoustic stack including an acoustical horn, a single half wavelength booster and a test specimen. Two aluminum alloys, namely extruded 2024-T351 and 7075-T6 alloys were used to machine test specimens in order to generate representative fatigue test data. Fatigue tests in the HCF and VHCF regime were successfully conducted and fatigue data was found in very good agreement with published corresponding data in 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".