Influences of fibre shape on the transverse modulus of unidirectional fibre reinforced composites using finite element and machine learning methods

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

hi@scite.ai

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

Henderson, NV 89052, USA

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.

Influences of fibre shape on the transverse modulus of unidirectional fibre reinforced composites using finite element and machine learning methods

Cited by 4 publications

References 19 publications

Artificial neural network prediction of transverse modulus in humid conditions for randomly distributed unidirectional fibre reinforced composites: A micromechanics approach

Artificial neural network prediction of transverse modulus in humid conditions for randomly distributed unidirectional fibre reinforced composites: A micromechanics approach

Buckling behaviors prediction of biological staggered composites with finite element analysis and machine learning coupled method

Exploring shear nonlinearity of plain-woven composites at various temperatures based on machine learning

Contact Info

Product

Resources

About