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Record W1649301931 · doi:10.4271/2005-01-3316

Composite Materials Inspection Methods, Equipment Selection

2005· article· en· W1649301931 on OpenAlex
Alemayehu Asfaw

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2005
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsProfessional Engineers Ontario
Fundersnot available
KeywordsSelection (genetic algorithm)Composite numberComputer scienceArtificial intelligenceAlgorithm

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">Tremendous research and development effort was put forth in the past to find suitable structural materials for aircraft designs. Several stages of development were encountered. Different materials were tested and applied, at the various stages, to meet specific design requirements - wood, steel, aluminum, titanium etc. The latest development in aircraft and aerospace structures is composite materials, which provide high strength, high stiffness, corrosion resistance, ease of fabrication, lower weight and eventual fuel savings. The strength/stiffness-to-weight ratio of composite materials has been proved higher than metals.</div> <div class="htmlview paragraph">Along with the development of materials, a very essential aspect is their maintenance and inspection. NASA, Boeing and other aircraft manufacturers have carried out a lot of research in the field. Only a few equipment are available at this time to enable effective inspection of composite material failures - delamination, disbonding, water ingestion, fiber fretting etc, which are quite different from metal failures.</div> <div class="htmlview paragraph">Airline carriers face various problems caused by changes in concepts, material processes and systems in maintenance whenever new materials are introduced. The introduction of composite materials has brought with it new concepts of inspection and maintenance procedures unknown when metals were used. Many air carriers, in collaboration with aircraft manufacturers, were forced to upgrade their maintenance philosophies in order to cope with the latest developments.</div> <div class="htmlview paragraph">Non-destructive-examination (NDE) is a major technique in composite aircraft structural materials inspection. Carefully evaluated and tested equipment for this purpose saves the airlines, especially small-to-medium sized, a lot of cost in terms of man-hours and acquisition and provides the best possible performance. Thermography is one of the best techniques developed. However, the cost of the equipment for this technique is high at this time.</div> <div class="htmlview paragraph">Equipment selection for NDE of composite materials is the major theme of the presentation. It suggests that development in the field needs more effort to make further studies to arrive at appropriate equipment choices for airlines and aircraft maintenance facilities.</div> <div class="htmlview paragraph">The main considerations in equipment selection are:</div> <div class="htmlview paragraph"> <ul class="list disc"> <li class="list-item"><div class="htmlview paragraph">purpose of inspection</div></li> <li class="list-item"><div class="htmlview paragraph">equipment capability</div></li> <li class="list-item"><div class="htmlview paragraph">cost of acquisition of the equipment</div></li> <li class="list-item"><div class="htmlview paragraph">safety considerations</div></li> <li class="list-item"><div class="htmlview paragraph">reliability</div></li> <li class="list-item"><div class="htmlview paragraph">equipment commonality</div></li> <li class="list-item"><div class="htmlview paragraph">training requirements</div></li> </ul> </div> <div class="htmlview paragraph">Discussion proceeds from the above considerations. Data collection and equipment manufacturing firms as well as experience related issues are raised. The necessity for physical evaluation tests and financial constraints are emphasized for final recommendation and decision.</div>

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.020
GPT teacher head0.296
Teacher spread0.275 · 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