Damage Tolerance Substantiation and Certification of Multiple Fastener Joints
Bibliographic record
Abstract
Damage tolerance (DT) requirements for rotorcraft structures are specified by FAA FAR 29.571 and 29.573 (Ref. 1). In broad terms, DT requires flight critical structure to tolerate certain damage without the damage becoming the source of crack growth and failure. The requirements can apply to multiple fastener joints (MFJ), such as riveted structures widely used in airframe applications, engine mounts, driveshafts, control tubes, etc. (both composite and metallic). Damage in such structures may be benign due to the multiplicity of fasteners to redistribute loads, and multiplicity of holes to stop the growth of damage. DT requirements for metallic MFJ structures are met by analysis of crack growth from the critical hole to failure. Stress intensity factors and material data support this type of analysis (Ref. 2-5). However, traditional analysis neglects the effects of adjacent holes and surrounding structure, which can produce inaccuracies. This paper presents a method for quickly simulating progressive growth of a crack from a rivet hole to an adjacent hole, and beyond. The method is more accurate than traditional methods based on stress intensity factor (SIF) solutions, and much more efficient than finite element methods. A test program was developed to validate the analytical approach, as well as to investigate potential improvements to MFJ designs for damage tolerance and fatigue. The testing employs a crack growth monitoring system that allows detection of multiple crack initiation locations and documentation of crack propagation in a continuous manner, on both sides of the specimen.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".