Fatigue and Reliability Assessment Incorporating Computer Strain Gage Network Data
Bibliographic record
Abstract
This paper details a procedure by which reliable engineering components may be designed and produced to withstand specified fatigue loading situations. The procedure is a modification of the current aircraft industry's damage tolerance approach as outlined, among others, by Goranson [1]. For the design and manufacture of a specific component, the procedure incorporates a knowledge or specification of: (1) a typical fatigue loading history; (2) the determination of the stress time history at a location or locations of most concern; (3) typical fatigue crack geometries; (4) the mechanical properties, including the fatigue crack growth and closure characteristics, of the material from which the component is or will be manufactured; and, (5) in stochastic process terms, a time or cycle dependent description of the inherent statistical scatter that always accompanies fatigue crack growth. While the procedure is quite general, its applicability is illustrated by an application to the improved design of mountain bicycle frames and components. Specifically and as an equivalent to standard aircraft flight-load histories such as TWIST, Mini-TWIST or FALSTAFF, the projected use of the results generated by the specialized data acquisition system whose development is described in a companion paper [2], is illustrated by estimating the fatigue crack growth characteristics and reliability of a mountain bicycle crank-arm. The procedure utilizes the loading history generated by smart strain gages situated on a mountain bicycle while the bicycle and rider are traversing a demanding mountainous trail. The acquisition system samples and captures strain data at 1 kHz with 12-bit resolution; performs peak detection and averaging calculations; transfers via digital radio the data to a Windows-based PC station at the trailhead; and analyzes these data both to determine stress profiles and to develop typical fatigue loading time-histories. Using this information, crack growth rate estimates based upon these load spectra, crack geometries typical of those found in mountain bicycle crank-arms, the crack closure concept, and the da/dN versus ΔK e f f for A17075-T6 may be obtained. Coupling this information with a stochastic process interpretation of the scatter in these crack growth estimates leads to a meaningful description of component reliability.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".