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Journal of Failure Analysis and Prevention

, Volume 19, Issue 3, pp 709–715 | Cite as

Optimization of the Layers of Composite Materials from Neural Networks with Tsai–Wu Failure Criterion

  • Camila Aparecida DinizEmail author
  • Sebastião Simões CunhaJr.
  • Guilherme Ferreira Gomes
  • Antônio Carlos AncelottiJr.
Technical Article---Peer-Reviewed
  • 63 Downloads

Abstract

The use of composite materials has increased lately and the need to know the behavior of these materials is very important once these devices are subject to suffer from damage such as cracks and delamination. Normally, to analyze failure problems in composite materials, the following steps are necessary: (1) structure geometry design, (2) numerical and/or experimental analysis and (3) use of failure criteria (e.g., Tsai–Wu failure criterion). If the used composite material has a non-expected failure criterion, the procedure must be repeated. In order to eliminate the procedure above, this study proposes the use of an artificial neural networks (ANN) inversion which can be used to determine an adequate configuration for the layers of the composite material from the desired failure criteria value. Numerical simulations, based on the finite element method, were made in order to create a database for ANN training and validation. After the inversion of the ANN, satisfactory results were obtained and this procedure could be used to minimize the high number of numerical simulations normally used in the design of a composite device.

Keywords

Artificial neural networks Tsai–Wu failure criterion Composite material Safety margin 

Notes

Acknowledgments

The authors would like to acknowledge the financial support from the Brazilian agency CAPES – Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.

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Copyright information

© ASM International 2019

Authors and Affiliations

  • Camila Aparecida Diniz
    • 1
    Email author
  • Sebastião Simões CunhaJr.
    • 1
  • Guilherme Ferreira Gomes
    • 1
  • Antônio Carlos AncelottiJr.
    • 1
  1. 1.Mechanical Engineering InstituteUniversidade Federal de Itajubá – UNIFEIItajubáBrazil

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