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Prediction of temperature-frequency-dependent mechanical properties of composites based on thermoplastic liquid resin reinforced with carbon fibers using artificial neural networks

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Abstract

A considerable interest has been generated in recent years in the use of thermoplastic polymers as matrices in the manufacture of advanced composites that require high reliability during long-term operations. In this research, a new Elium® acrylic matrix developed by Arkema was studied to evaluate the accelerated test methodology based on time-temperature superposition principle of Carbon Fiber/Elium® 150 composites. The results show that the high frequencies increase the glass transition (Tg) to higher values because the free volume is favored by polymer chains movement. In addition, artificial neural network has been used to model the temperature-frequency dependence of dynamic mechanical over the wide range of temperatures and frequencies due to its complex non-linear behavior. It has been observed that low frequencies result in low damping due to the lower internal friction, while high frequencies provide greater stiffness to the chains, resulting in a high damping. The long-term life prediction using master curves confirms that this new material can be considered to acoustic or vibrational damping purposes, considering its use in temperatures above Tg.

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Acknowledgments

The authors are grateful to the Brazilian Funding Institutions FINEP (Financier of Studies and projects), CAPES (Improvement Coordination of Higher Level Personnel), and FAPEMIG (research supporting foundation of Minas Gerais state—Grant number APQ-00385-18) for the financial supports.

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Authors and Affiliations

  1. Federal University of Itajuba, Mechanical Institute, Itajuba, MG, Brazil

    Lorena Cristina Miranda Barbosa, Guilherme Gomes & Antonio Carlos Ancelotti Junior

  2. Nucleus of Technology in Composites, NTC, São José dos Campos, Brazil

    Lorena Cristina Miranda Barbosa & Antonio Carlos Ancelotti Junior

Authors
  1. Lorena Cristina Miranda Barbosa
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  2. Guilherme Gomes
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  3. Antonio Carlos Ancelotti Junior
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Correspondence to Lorena Cristina Miranda Barbosa.

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Barbosa, L.C.M., Gomes, G. & Junior, A.C.A. Prediction of temperature-frequency-dependent mechanical properties of composites based on thermoplastic liquid resin reinforced with carbon fibers using artificial neural networks. Int J Adv Manuf Technol 105, 2543–2556 (2019). https://doi.org/10.1007/s00170-019-04486-4

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  • DOI: https://doi.org/10.1007/s00170-019-04486-4

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