Taguchi’s DOE and artificial neural network analysis for the prediction of tribological performance of graphene nano-platelets filled glass fiber reinforced epoxy composites under the dry sliding condition

Title
Taguchi’s DOE and artificial neural network analysis for the prediction of tribological performance of graphene nano-platelets filled glass fiber reinforced epoxy composites under the dry sliding condition
Authors
Keywords
-
Journal
TRIBOLOGY INTERNATIONAL
Volume 172, Issue -, Pages 107580
Publisher
Elsevier BV
Online
2022-04-15
DOI
10.1016/j.triboint.2022.107580

Ask authors/readers for more resources

Create your own webinar

Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.

Create Now

Become a Peeref-certified reviewer

The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.

Get Started