Skip navigation
DSpace logo

  1. IDR@IIT Jammu
  2. IITJmu Scholarly Publications
  3. Journal Article
Please use this identifier to cite or link to this item: http://10.10.120.238:8080/xmlui/handle/123456789/623
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKushvaha V.en_US
dc.contributor.authorKumar S.A.en_US
dc.contributor.authorMadhushri P.en_US
dc.contributor.authorSharma A.en_US
dc.date.accessioned2023-11-30T08:43:21Z-
dc.date.available2023-11-30T08:43:21Z-
dc.date.issued2020-
dc.identifier.issn0021-9983-
dc.identifier.otherEID(2-s2.0-85081951714)-
dc.identifier.urihttps://dx.doi.org/10.1177/0021998320911418-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/623-
dc.description.abstractIn this paper, the artificial neural network technique using a multi-layer perceptron feed forward scheme was used to model and predict the mode-I fracture behaviour of particulate polymer composites when subjected to impact loading. A neural network consisting of three-layers was employed to develop the network. Artificial neural network was constructed using six input parameters such as shear wave speed (CS), density (D), elastic modulus (Ed), longitudinal wave speed (CL), volume fraction (Vf) and time (t). The influence of input parameters on the output stress intensity factor and crack-initiation fracture toughness were found to be in the order of t &gten_US
dc.description.abstractCS &gten_US
dc.description.abstractD &gten_US
dc.description.abstractEd &gten_US
dc.description.abstractCL &gten_US
dc.description.abstractVf. The degree of accuracy of prediction was 92.7% for stress intensity factor. In this regard, artificial neural network can be used in the modelling and prediction of fracture behaviour of particulate polymer composites under impact loading. © The Author(s) 2020.en_US
dc.language.isoenen_US
dc.publisherSAGE Publications Ltden_US
dc.sourceJournal of Composite Materialsen_US
dc.subjectArtificial neural networken_US
dc.subjectdynamic fracture toughnessen_US
dc.subjectimpact loadingen_US
dc.subjectmodellingen_US
dc.subjectpolymer compositeen_US
dc.subjectpredictionen_US
dc.subjectstress intensity factoren_US
dc.titleArtificial neural network technique to predict dynamic fracture of particulate compositeen_US
dc.typeJournal Articleen_US
Appears in Collections:Journal Article

Files in This Item:
There are no files associated with this item.
Show simple item record


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.