An Update on the Impact of Forging Residual Stress in Airframe Component Design
Bibliographic record
Abstract
Abstract In a recently concluded study, the effects of forging process–induced bulk residual stresses on fatigue life were evaluated at both the coupon and large component level. During this program, it was demonstrated that the extraction of confounding residual stress effects from material property data (especially fatigue crack growth rate data), coupled with the explicit inclusion of forging residual stresses in subsequent fatigue analyses, resulted in a significant improvement in the fidelity of those analyses. In the first phase of the program, coupon-level tests were carefully designed, executed, and analyzed, and it was shown that the newly developed methods resulted in analysis versus test life correlation ratios that were either within the United States Air Force (USAF) required scatter factor of 2, or were conservative. This is in contrast to a much broader scatter band (5x) for calculations made using traditional (non–residual-stress-informed) methods. The explicit residual stress method was incorporated into an integrated structural design/analysis tool suite that allows zoning of parts into residual stress–specific regions, automated generation of location-specific fatigue spectra, automated execution of fatigue crack initiation, fatigue crack growth, and residual strength analyses, and automated generation of fatigue-based design, allowable stresses, and margins of safety. In the second phase of the program, the residual stress design procedure was applied (to the furthest extent possible) to the design and manufacture of a large, fighter aircraft bulkhead. The objective of this phase of the program was to demonstrate, by way of two large component fatigue tests, that the technology would scale to the structural level, and that its use would result in components that are either lighter or more durable (or both) than their traditionally designed counterparts. In this article, we compare and contrast the traditional versus residual stress–informed design procedures, and we describe in detail the resulting baseline and “optimized” test articles. A full description of the fatigue tests, along with comparisons between detailed fatigue calculations and test findings, are given.
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 | 0.004 | 0.008 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".