(Formula presented.) is a assessing the stress intensity factor (SIF) threshold of materials from specimens failed in the very-high-cycle fatigue (VHCF) region and showing the typical fish-eye morphology. The developed process involves two key parameters: the size of the optically dark area (ODA), which is a typical feature region on the fracture surface of specimens failing in the VHCF life region from internal defects, and the stress amplitude at the crack initiation site. Based on the optical image of the fracture surface input from the user side, the automatic detection of the ODA feature is obtained with a deep learning method. Thereafter, the analytical stress distribution in the specimen is assessed, thus allowing to compute the critical SIF threshold for the investigated specimen. This package requires minimal scanning prerequisites on specimens, featuring notable advantages in modularity, automation, and practical usability.

CALC−ΔKth: Automatic Assessment of the Stress Intensity Factor Threshold From VHCF Fracture Surfaces With Optically Dark Area / Li, R.; Zhang, W.; Paolino, D.; Tridello, A.; Berto, F.; Peng, Y.. - In: FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES. - ISSN 8756-758X. - 48:9(2025), pp. 4071-4083. [10.1111/ffe.14701]

CALC−ΔKth: Automatic Assessment of the Stress Intensity Factor Threshold From VHCF Fracture Surfaces With Optically Dark Area

Berto F.;
2025

Abstract

(Formula presented.) is a assessing the stress intensity factor (SIF) threshold of materials from specimens failed in the very-high-cycle fatigue (VHCF) region and showing the typical fish-eye morphology. The developed process involves two key parameters: the size of the optically dark area (ODA), which is a typical feature region on the fracture surface of specimens failing in the VHCF life region from internal defects, and the stress amplitude at the crack initiation site. Based on the optical image of the fracture surface input from the user side, the automatic detection of the ODA feature is obtained with a deep learning method. Thereafter, the analytical stress distribution in the specimen is assessed, thus allowing to compute the critical SIF threshold for the investigated specimen. This package requires minimal scanning prerequisites on specimens, featuring notable advantages in modularity, automation, and practical usability.
2025
deep learning; optically dark area; stress intensity factor threshold; very-high-cycle fatigue
01 Pubblicazione su rivista::01a Articolo in rivista
CALC−ΔKth: Automatic Assessment of the Stress Intensity Factor Threshold From VHCF Fracture Surfaces With Optically Dark Area / Li, R.; Zhang, W.; Paolino, D.; Tridello, A.; Berto, F.; Peng, Y.. - In: FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES. - ISSN 8756-758X. - 48:9(2025), pp. 4071-4083. [10.1111/ffe.14701]
File allegati a questo prodotto
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1748237
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact