Fatigue residual useful life estimation of Ni-base alloy weld with time-series data

Park, Jae Phil; Ham, Junhyuk; Kim, Ji Hyun; Oh, Young-Jin; Bahn, Chi Bum

doi:10.1007/s12206-023-0412-y

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Fatigue residual useful life estimation of Ni-base alloy weld with time-series data

Cited by 2 publications

References 27 publications

Machine learning approach for predicting and understanding fatigue crack growth rate of austenitic stainless steels in high-temperature water environments

Machine learning approach for predicting and understanding fatigue crack growth rate of austenitic stainless steels in high-temperature water environments

Machine Learning Approach for Predicting and Understanding Fatigue Crack Growth Rate of Austenitic Stainless Steels in High-Temperature Water Environments

Contact Info

Product

Resources

About