Neural Network based Strength Assessment of Epoxy-Graphene Oxide Composites for Unmanned Aerial Vehicle Applications
Main Article Content
Abstract
Using the hand layup process, a polymer matrix composite (PMC) laminate comprising epoxy resin systems and graphene oxide (GO) reinforced with glass fibre was created. The produced laminate is then further cut into test specimens as per the ASTM standards and its stiffness and strength are evaluated. An attempt has been made to investigate how the tensile strength, flexural strength, Youngs modulus and percentage elongation of the composite laminate were changed by various volumetric additions of GO to the epoxy matrix. A close agreement between the neural network predictions and the numerical computations of tensile and flexural strengths in PMC with GO fillers suggests a high level of model accuracy and validity.
Article Details
Issue
Section
Articles
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms: a. Authors retain copyright and grant the journal right of first publication, with the work two years after publication simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).