Failure prediction and optimization for composite pressure vessel combining FEM simulation and machine learning approach

Wang, Qingfeng; Qin, Huasong; Jia, Liyong; Li, Ziyi; Zhang, Guoqiang; Li, Yushu; Liu, Yilun

doi:10.1016/j.compstruct.2024.118099

Composite Structures

2024

DOI: 10.1016/j.compstruct.2024.118099

|View full text |Cite

Failure prediction and optimization for composite pressure vessel combining FEM simulation and machine learning approach

Qingfeng Wang,

Huasong Qin,

Liyong Jia

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 8 publications

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Reliability-based composite pressure vessel design optimization with cure-induced stresses and spatial material variability

Van Bavel,

Shishkina,

Vandepitte

et al. 2024

Computer Methods in Applied Mechanics and Engineering

View full text Add to dashboard Cite

Reliability-based composite pressure vessel design optimization with cure-induced stresses and spatial material variability

Van Bavel,

Shishkina,

Vandepitte

et al. 2024

Computer Methods in Applied Mechanics and Engineering

View full text Add to dashboard Cite

Deep learning-driven predictive tools for damage prediction and optimization in composite hydrogen storage tanks

Nachtane,

El Fallaki Idrissi,

Tarfaoui

et al. 2024

Composites Communications

View full text Add to dashboard Cite

Sensors integration for structural health monitoring in composite pressure vessels: A review

Meemary,

Vasiukov,

Deléglise-Lagardère

et al. 2025

Composite Structures

View full text Add to dashboard Cite

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.

Failure prediction and optimization for composite pressure vessel combining FEM simulation and machine learning approach

Cited by 8 publications

References 74 publications

Reliability-based composite pressure vessel design optimization with cure-induced stresses and spatial material variability

Reliability-based composite pressure vessel design optimization with cure-induced stresses and spatial material variability

Deep learning-driven predictive tools for damage prediction and optimization in composite hydrogen storage tanks

Sensors integration for structural health monitoring in composite pressure vessels: A review

Contact Info

Product

Resources

About