A Review of Fatigue and Damage Tolerance Life Prediction Methodologies toward Certification of Additively Manufactured Metallic Principal Structural Elements
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-1509.vid This review evaluates approaches towards acceptable methods for fatigue and damage tolerance (F&DT) substantiation of additively manufactured (AM) metallic structural components and the efforts towards their qualification and certification (Q&C). This review further highlights potential gaps in meeting the airworthiness certification requirements and identifies where the guidelines are lacking for Q&C of aerospace metallic principal structural elements (PSE). It highlights the formidable task that F&DT analysis criterion must achieve for minimum structural reliability requirements for PSEs – structural components whose failure could result in catastrophic flight safety results. Invariably, this paper sheds light on progress towards a solution to perhaps the greatest challenge in F&DT of AM metallic parts – quantifying their material strength allowables. By further focusing on the powder bed fusion AM process, this paper reviews advances in material strength variability studies resulting from this process including residual thermal stresses, porosity, comprehensive microstructural defects, surface roughness and deformations such as shrinkage and warpage associated with the metal powder bed fusion process. Thus, the survey includes a consideration of the design and build aspects of metal powder bed fusion and the approaches towards optimally configured process parameters that consistently meet minimum structural reliability requirements for certification by airworthiness authorities.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".