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Enregistrement W7133021426

Robotization of eddy current surface inspection in the aerospace industry

2025· dissertation· en· W7133021426 sur OpenAlexaff
Ilkka Keskinen

Notice bibliographique

RevueTrepo - Institutional Repository of Tampere University · 2025
Typedissertation
Langueen
DomaineEngineering
ThématiqueNon-Destructive Testing Techniques
Établissements canadiensOffice of the Chief Medical Examiner
Organismes subventionnairesnon disponible
Mots-clésEddy-current testingAerospaceReliability (semiconductor)Eddy currentConsistency (knowledge bases)Process (computing)Nondestructive testingFatigue testing
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Service-Induced cracks in metallic aircraft structures caused by fatigue are a common problem. If fatigue cracks remain undetected long enough and therefore untreated, they can cause a catastrophic failure of the structure, which threatens the safety of flight. To prevent failure from happening and to detect these possible fatigue cracks, periodic inspections are carried out. Eddy current testing is the primary non-destructive testing method used in the aerospace industry to detect such surface opening fatigue cracks in mostly aluminium and titanium structures. Traditionally the eddy current inspection is conducted manually by non-destructive testing inspector. Automating non-destructive inspection, which is necessary but expensive, time consuming and prone to human factors process, could provide many benefits over the manual inspection in certain inspection areas that are well suited for automation. The most important benefit would be the consistency of the inspections. When the inspection process is consistent, the quality of it can be measured and therefore the reliability can be assessed. Time consumption of the inspection would not be necessarily considerably reduced, unless the batch sizes are large but the time during which the inspection is conducted could be assigned for example during off business hours when it does not interfere with other work tasks. The human factors, such as fatigue and stress would not be factoring to the results of the inspection, which would increase the consistency and reliability of the inspection. Thanks to the lesser workload on the non-destructive testing inspectors and interference to other maintenance tasks the costs of eddy current inspection would be reduced. The aim of this thesis was to study how eddy current inspection of the CASE specimens could be automated. Literature review was conducted to find out how non-destructive methods have been automated previously in the industry. It was discovered that the use of industrial robots to conduct the inspection was a viable solution in many cases with different non-destructive methods, including ultrasonic testing and eddy current testing. It was suspected that additional sensors were necessary for the robot to carry out the inspection. A force torque sensor was used to ensure that the robot was able to conduct the inspection properly. Based on the findings of the literature review an experimental robotized eddy current surface inspection was conducted on the two CASE specimens provided by the client company. Preparation for the experiment included manufacturing tool holder for the robot used in the experiment. Also fixtures for the CASE specimens were manufactured. The most significant results when analysing the viability of applying robotized inspection for the CASE specimen are the quality of eddy current inspection signal and the inspection coverage. The inspection signal quality was analysed against a reference calibration signal received from a standard calibration block. The inspection coverage on the other hand was analysed and measured visually, based on videos, photographs and physical measurements taken from the experiment.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,646
Score d'incertitude au seuil0,922

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,012
Tête enseignante GPT0,232
Écart entre enseignants0,220 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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