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Failure Analysis of Aircraft Landing Gear Components

2019· book-chapter· en· W3093730671 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueASM International eBooks · 2019
Typebook-chapter
Languageen
FieldEngineering
TopicMechanical Failure Analysis and Simulation
Canadian institutionsnot available
Fundersnot available
KeywordsLanding gearFatigue testingEngineeringCorrosion fatigueForensic engineeringFailure causesReliability engineeringCorrosionStructural engineering

Abstract

fetched live from OpenAlex

Abstract Despite extensive aircraft landing gear design analyses and tests performed by designers and manufacturers, and the large number of trouble-free landings, aircraft users have experienced problems with and failures of landing gear components. Different data banks and over 200 failure analysis reports were surveyed to provide an overview of structural landing gear component failures as experienced by the Canadian Forces over the last 20 years on more than 20 aircraft types, and to assess trends in failure mechanisms and causes. Case histories were selected to illustrate typical problems, troublesome failure mechanisms, the role of high strength aluminum alloys and steels, and situations where fracture mechanics analyses provided insight into the failures. The two main failure mechanisms were: fatigue occurring mainly in steel components, and corrosion related problems with aluminum alloys. Very few overload failures were noted. A number of causes were identified: design deficiencies and manufacturing defects leading mainly to fatigue failures, and poor materials selection and improper maintenance as the principal causes of corrosion-related failures. The survey showed that a proper understanding of the failure mechanisms and causes, by thorough failure analysis, provides valuable feedback information to designers, operators and maintenance personnel for appropriate corrective actions to be taken.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.798
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.016
GPT teacher head0.223
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it