ENGLISH
La vitrine de diffusion des publications et contributions des chercheurs de l'ÉTS
RECHERCHER

Modeling of the microstructure alteration induced by hard turning of Inconel 718

Téléchargements

Téléchargements par mois depuis la dernière année

Plus de statistiques...

Touazine, Heithem, Akab, Jordan, Jahazi, Mohammad, Tahan, Antoine, Jomaa, Walid et Bocher, Philippe. 2017. « Modeling of the microstructure alteration induced by hard turning of Inconel 718 ». International Journal of Advanced Manufacturing Technology, vol. 93, nº 9-12. pp. 3705-3712.
Compte des citations dans Scopus : 6.

[thumbnail of Jahazi M 2017 15680 Modeling of the microstructure alteration induced.pdf]
Prévisualisation
PDF
Jahazi M 2017 15680 Modeling of the microstructure alteration induced.pdf - Version acceptée
Licence d'utilisation : Tous les droits réservés aux détenteurs du droit d'auteur.

Télécharger (751kB) | Prévisualisation

Résumé

The machining of nickel-based superalloys used for aeronautical applications generates damage, deformation, and carbide cracking in machined surface and subsurface layers as a result of microstructural heterogeneities which reduce the fatigue life of aeronautic machined components. In this study, Inconel 718 was hard turned with a carbide tool using different cutting conditions according to a Roquemore 311B hybrid design of experiments (DOE) method. The main objective of the study was to model the effect of cutting parameters on the evolution of the microstructure and to accurately predict the alterations induced by machining, especially the deformed layer thickness (DL) and the average number of cracked carbides (ACC). The material removal rate (MRR) and the deformation power (E) were calculated in order to obtain a strong correlation between controlled cutting parameters and microstructure alterations. Damages were quantified using a confocal laser-digital microscope and were validated with the proposed models. These models showed a direct relation between both MRR and E with DL and ACC, with good prediction at a 95% confidence interval (CI).

Type de document: Article publié dans une revue, révisé par les pairs
Professeur:
Professeur
Jahazi, Mohammad
Tahan, Antoine
Bocher, Philippe
Affiliation: Génie mécanique, Génie mécanique, Génie mécanique
Date de dépôt: 24 août 2017 13:35
Dernière modification: 13 avr. 2023 16:08
URI: https://espace2.etsmtl.ca/id/eprint/15680

Actions (Authentification requise)

Dernière vérification avant le dépôt Dernière vérification avant le dépôt