Skip to main content
padlock icon - secure page this page is secure

Experimental study and prediction using ANN on mass loss of hybrid composites

Buy Article:

$40.95 + tax (Refund Policy)

Purpose ‐ The purpose of this paper is to produce Al6061 metal matrix composites reinforced with silicon carbide (SiC) and graphite particulates and study their wear behavior and also to develop artificial neural network model to predict the mass loss of hybrid composites. Design/methodology/approach ‐ The hybrid composites were produced by using stir casting process. The experiments were conducted based on the central composite rotatable design matrix using pin-on-disc wear testing machine. The set of data collected from the experimental values were used to train a back propagation (BP) learning algorithm with one hidden layer network. In artificial neural network (ANN) training module, four input vectors were used in the construction of proposed network namely, weight percentage of SiC particles, weight percentage of graphite particles, applied load and sliding distance. Mass loss was the output to be obtained from the proposed network. After training process, the test data collected from the experimental values were used to check the accuracy of proposed ANN model. Findings ‐ The results show that the well trained one hidden layer network have smaller training errors and much better generalization performance and can be successfully used for the prediction of mass loss of hybrid aluminium metal matrix composites. Originality/value ‐ In this paper the ANN method was adopted to predict the mass loss of hybrid composites. It was found that artificial neural network can be successfully used for prediction of mass loss of composites.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: Aluminium matrix composites; Artificial neural network; Back propagation; Composite materials; Hybrid composites; Mass loss; Metals; Neural nets; Wear

Document Type: Research Article

Publication date: April 20, 2012

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more