Neural Networks Based Fatigue Life Prediction of Multi Walled Carbon Nano Tubes Doped E-Glass/Epoxy Laminates
Main Article Content
Abstract
In recent years, carbon nano tubes and their applications have been the prime focus of research in the field of nanotechnology. In this paper, the fatigue life prediction of multi walled carbon nano tubes (MWCNT) doped E-Glass/Epoxy laminates is presented. Two different doping ratios of MWCNT (0.2% and 0.4%) are considered to demonstrate the improvement in fatigue life of the composite laminate. The fatigue tests are undertaken on an Instron 8802 universal testing machine using a uni-directional (UD) Glass/Epoxy laminate specimen fabricated as per the ASTM standard - ASTM D 3039. An artificial neural network (ANN) based approach with a back propagation algorithm is used to predict the fatigue life cycles. The proposed neural network is trained using the fatigue test data set. The predicted fatigue results from the ANN are in good agreement with the experimental results. The proposed approach can be utilised to predict the fatigue life of Glass/Epoxy laminates for varied MWCNT doping ratios.
Article Details
Issue
Section
Articles
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).