Lifetime Prediction of Single Crystal Nickel-Based Superalloys

Loading...
Thumbnail Image

Date

2025

Journal Title

Journal ISSN

Volume Title

Publisher

Mdpi

Open Access Color

GOLD

Green Open Access

Yes

OpenAIRE Downloads

OpenAIRE Views

Publicly Funded

No
Impulse
Average
Influence
Average
Popularity
Top 10%

Research Projects

Journal Issue

Abstract

Single crystal nickel-based superalloys are extensively used in turbine blade applications due to their superior creep resistance compared to their polycrystalline counterparts. With the high creep resistance, high cycle fatigue (HCF) and low cycle fatigue (LCF) become primary failure mechanisms for such applications. This study investigates the fatigue life prediction of CMSX-4 using a combination of crystal plasticity and lifetime assessment models. The constitutive crystal plasticity model simulates the anisotropic, rate-dependent deformation behavior of CMSX-4, while the modified Chaboche damage model is used for lifetime assessment, focusing on cleavage stresses on active slip planes to include anisotropy. Both qualitative and quantitative data obtained from HCF experiments on single crystal superalloys with notched geometry were used for validation of the model. Furthermore, artificial neural networks (ANNs) were employed to enhance the accuracy of lifetime predictions across varying temperatures by analyzing the fatigue curves obtained from the damage model. The integration of crystal plasticity, damage mechanics, and ANNs resulted in an accurate prediction of fatigue life and crack initiation points under complex loading conditions of single crystals superalloys.

Description

Kaftancioglu, Utku/0009-0009-6387-1990; BAYRAKTAR, Emin/0000-0003-0644-5249

Keywords

crystal plasticity, artificial neural networks, lifetime assessment modelling, turbine blades, crystal plasticity, Technology, QH301-705.5, T, Physics, QC1-999, Engineering (General). Civil engineering (General), turbine blades, Chemistry, lifetime assessment modelling, TA1-2040, Biology (General), artificial neural networks, QD1-999

Turkish CoHE Thesis Center URL

Fields of Science

02 engineering and technology, 0205 materials engineering, 0203 mechanical engineering

Citation

WoS Q

Q2

Scopus Q

Q2
OpenCitations Logo
OpenCitations Citation Count
N/A

Source

Applied Sciences

Volume

15

Issue

1

Start Page

201

End Page

Collections

PlumX Metrics
Citations

Scopus : 2

Captures

Mendeley Readers : 7

Google Scholar Logo
Google Scholar™
OpenAlex Logo
OpenAlex FWCI
0.81663987

Sustainable Development Goals