Identification of impact loading characteristics of composite laminated cylindrical shells using neural networks

  • Authors:

  • Y.-S. Lee;C.-H. Ryu;C.-M. Myung

  • Affiliations:

  • Department of Mechanical Design Engineering, ChungNam National University, DaeJeon, Korea;Department of Mechanical Design Engineering, ChungNam National University, DaeJeon, Korea;Technical Information Department, Agency for Defense Development, DaeJeon, Korea

  • Venue:
  • ICECT'03 Proceedings of the third international conference on Engineering computational technology
  • Year:
  • 2002

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Abstract

A study on the structural analysis of the composite laminated cylindrical shell which has simply supported boundary conditions at both ends, was performed. The results were used into the neural networks. Neural networks identify the loading characteristics of the composite shell. Momentum backpropagation which the learning rate can be varied was developed. Input patterns consist of strains at 9 side points which is divided equally. Output layers are loading point, loads, maximum displacements at impact point. Neural networks consist of 3 hidden layers. Developed program was used for the training. The training with variable learning rate was converged close to real characteristics. As a result, identifications of loading characteristics are available with 99.7% confidence interval, 11.6% on impact loads, 4.2% on loading points, 17.8% on maximum deflections. Inverse engineering can be applicable to the composite laminated cylindrical shells with developed neural networks.