Feasibility of ultrasonic phased array inspection on as-forged components
Notice bibliographique
Résumé
In the context of ultrasonic inspection of aircraft engine components, this work investigates the feasibility of performing ultrasonic non-destructive testing on as-forged parts. In partnership with Pratt and Whitney Canada (P&WC), a case study on fan disk forgings that require an extremely high inspection standard was proposed. Therefore, the challenging ultrasonic inspection of parts with complex geometries was researched and developed in three complementary works. First, the reliability of the total focusing method (TFM) applied to specimens with concave and convex profiles was studied in terms of the probe standoff. Artifacts with high amplitude and resolution loss occurred at some profile-standoff combinations. So, a probe standoff optimization method (PSOM) was introduced to mitigate such effects. As a result, considerable TFM improvements were demonstrated at the optimal standoff. The TFM image of a convex specimen had its array performance indicator (API) improved and the signal-to-artifact ratio (SAR) gained up to 13 dB. Lastly, the image under a concave surface gained up to 33 dB in signal-to-artifact ratio. The second study aimed at optimizing the imaging of defects below parts with concave surfaces. A beamforming strategy was proposed to mitigate the poor ultrasound penetration problem. In this method, the refraction was compensated by focusing the ultrasonic beam on points on the specimen’s surface. Using fewer transmissions, the beam computation showed a gain of 12 dB per transmission, and the imaging of a 1 mm side drilled hole presented an improvement of approximately 11 dB in signal-to-noise ratio. The third article presents a novel global TFM (gTFM) to scan a complex specimen representing an aerospace disk forging mock-up. This specimen contained 30 side-drilled holes as sensitivity targets and a profile geometry comprised of multiple concave and convex surfaces. This study combined both previous works through a digital twin of the inspection to optimize the scan plan. Moreover, the ultrasonic phased array probe was scanned around the specimen using a robotic arm, and all acquisitions were combined to generate the global TFM images. The comparison of different scan plans showed that the optimal one, with the probe position adapted to the surface profile, resulted in the sharpest image of the SDHs. This represents a 40% increase in the mean contrast-to-noise ratio (CNR), a 70% reduction in position error (only 0.1mm), and a 33% reduction in the array performance indicator (API). The inspection coverage was efficient, with only seven probe positions needed to depict the complete cross-section of the specimen. Finally, the results of the research on P&WC forgings are presented. The Ni-alloy was characterized and assessed using standard and phased array ultrasonic testing. Then a bore inspection using TFM was proposed and validated as an alternative to increase the inspection sensitivity. A flaw caused in manufacturing was evaluated and sized using a CT scan, standard ultrasonic testing, and TFM. The latter presented the best results with a size error below 10%. To complete the study, an experiment for comparison of sensitivity was designed for P&WC.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».