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Design Optimization of Composite Prosthetic Tubes Using GA-ANN Algorithm Considering Tsai-Wu Failure Criteria

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Abstract

The investigation of possible failures in composite materials is a matter of very great importance, and the Tsai-Wu criterion is an effective criterion for analyzing those flaws in anisotropic materials and defining whether the material at a given load will or will not suffer structural failure. In this study, an optimization procedure is proposed to minimize the maximum value of Tsai-Wu of laminated composite tubes subject to axial loading. Artificial neural networks and genetic algorithms are chosen as optimization tools. The results of this study show that the developed algorithm converges faster. Then, the maximum Tsai-Wu value is used as the objective function and the fiber orientations are the constraints in the optimization process. The results yielded by them are compared and discussed. Optimal results are compared with respect to the usual initial design. The design approach is recommended for structures where composites are the key load-carrying members such as orthopedic prosthesis.

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Acknowledgments

The authors would like to acknowledge the financial support from the Brazilian agency CNPq—Conselho Nacional de Desenvolvimento Científico e Tecnológico and CAPES—Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

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Authors and Affiliations

  1. Mechanical Engineering Institute, Federal University of Itajubá, Av. BPS, 1303, Itajubá, Brazil

    Guilherme Ferreira Gomes, Camila Aparecida Diniz, Sebastião Simões da Cunha Jr. & Antonio Carlos Ancelotti Jr.

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  1. Guilherme Ferreira Gomes
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  2. Camila Aparecida Diniz
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  3. Sebastião Simões da Cunha Jr.
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  4. Antonio Carlos Ancelotti Jr.
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Correspondence to Guilherme Ferreira Gomes.

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Gomes, G.F., Diniz, C.A., da Cunha, S.S. et al. Design Optimization of Composite Prosthetic Tubes Using GA-ANN Algorithm Considering Tsai-Wu Failure Criteria. J Fail. Anal. and Preven. 17, 740–749 (2017). https://doi.org/10.1007/s11668-017-0304-5

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  • DOI: https://doi.org/10.1007/s11668-017-0304-5

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