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GENETIC ALGORITHM OPTIMIZATION METHOD FOR PARAMETER ESTIMATION IN THE MODELING OF STORAGE MODULUS OF THERMOPLASTICS

Yıl 2019, Cilt: 37 Sayı: 3, 981 - 988, 01.09.2020

Öz

Polymeric materials exhibit temperature and rate dependent behavior. Therefore, in the modeling of polymeric and polymer based composite materials, the mechanical properties should be defined as rate and temperature dependent to be able to capture the behaviors. Dynamic mechanical analysis (DMA) is mostly used method to determine the viscous and elastic properties via loss and storage modulus. The storage modulus obtained from DMA and the elasticity moduli relate to the same physical phenomena. Even though the magnitudes of elasticity modulus and the storage modulus are not the same, the variation of modulus values with varying temperature follows the same trend. Therefore in the modeling of polymeric material behaviors, temperature and rate dependent elasticity modulus can be determined using DMA results. The objective of this work is to model the storage modulus which can be used as the elasticity modulus with a proper shift in the material models. For that purpose, a semi crystalline Polypropylene (PP) and amorphous plasticized Polyvinyl Chloride (PPVC) thermoplastics are selected to show the applicability of the model for amorphous and semi crystalline polymers. Different from many works in the literature, Genetic algorithm (GA) optimization method is used for parameter estimation in the modeling of storage modulus of PPVC and PP. The parameter optimization procedure is successfully implemented for the case of two polymers. The experimental storage modulus versus temperature curves of PP and PPVC obtained from [1, 2] respectively are used for validation. Good match with experimental data is observed. The rubbery state and rubbery flow observed in semi crystalline PP and sudden drop in the modulus around the glass transition temperature seen in amorphous PPVC are successfully simulated.

Kaynakça

  • [1] Wang K., Ahzi S., Boumbimba R. M., Bahlouli N., Addiego F., Remond, Y. (2013) Micromechanical modeling of the elastic behavior of polypropylene based organoclay nanocomposites under a wide range of temperatures and strain rates/frequencies, Mechanics of Materials, 64, 56–68.
  • [2] Bernard C. A., Bahlouli N., Wagner-Kocher J., Lin J., Ahzi S., and Remond Y. (2018) Multiscale description and prediction of the thermomechanical behavior of multilayered plasticized PVC under a wide range of strain rate, Mater Sci Polymers, 1-16.
  • [3] Bikiaris D., (2010) Microstructure and Properties of Polypropylene/Carbon Nanotube Nanocomposites, Materials, 3, 2884-2946.
  • [4] Sharma S. K., Nayak S. (2009) Surface modified clay/polypropylene (PP) nanocomposites: Effect on physico-mechanical, thermal and morphological properties, Polymer Degradation and Stability, 94, 132–138.
  • [5] Colak O. U., Acar A., (2013) Modeling of hydro-thermo-mechanical behavior of Nafion NRE212 for Polymer Electrolyte Membrane Fuel Cells using the Finite Viscoplasticity Theory Based on Overstress for Polymers (FVBOP), Mech. Time-Dependent Materials, 17:331–347
  • [6] Richeton J., Ahzi S., Vecchio k.S., Jiang F.C., Makradi, A. (2007) Modeling and validation of the large deformation inelastic response of amorphous polymers over a wide range of temperatures and strain rates, International Journal of Solids and Structures 44, 7938–7954.
  • [7] Sekercioglu T., Canyurt O. E. (2013) Development of the positive mean stress diagrams using genetic algorithm approach, Fatigue & Fracture of Engineering Materials & Structures, 37, 306-313.
  • [8] Wardeh M. and Toutanji H. A. (2015) Parameter estimation of an anisotropic damage model for concrete using genetic algorithms, International Journal of Damage Mechanics, 1–25.
  • [9] Guo Y., ·Meng G., Li, H. (2009) Parameter determination and response analysis of viscoelastic material, Arch Appl Mech, 79, 147–155.
  • [10] Dusunceli N., Colak O., Filiz, C. (2010) Determination of material parameters of a viscoplastic model by genetic algorithm, Materials and Design, 31, 1250–1255.
  • [11] Andrade-Campos, A., Thuillier, S., Pilvin, P., Teixeira-Dias, F.., (2007) On the determination of material parameters for internal variable thermoelastic–viscoplastic constitutive models. International Journal of Plasticity, 23, 1349–1379.
  • [12] Reeves, C., Rowe, J. E., (2002) Genetic algorithms: Principles and Perspectives, A Guide to GA Theory, Springer, Boston, MA.
  • [13] Colak O. U., Ahzi S. and Remond Y., (2013) Cooperative viscoplasticity theory based on overstess approach for modeling large deformation behavior of amorphous polymers, Polymer International, 62, 1560-1565.
Yıl 2019, Cilt: 37 Sayı: 3, 981 - 988, 01.09.2020

Öz

Kaynakça

  • [1] Wang K., Ahzi S., Boumbimba R. M., Bahlouli N., Addiego F., Remond, Y. (2013) Micromechanical modeling of the elastic behavior of polypropylene based organoclay nanocomposites under a wide range of temperatures and strain rates/frequencies, Mechanics of Materials, 64, 56–68.
  • [2] Bernard C. A., Bahlouli N., Wagner-Kocher J., Lin J., Ahzi S., and Remond Y. (2018) Multiscale description and prediction of the thermomechanical behavior of multilayered plasticized PVC under a wide range of strain rate, Mater Sci Polymers, 1-16.
  • [3] Bikiaris D., (2010) Microstructure and Properties of Polypropylene/Carbon Nanotube Nanocomposites, Materials, 3, 2884-2946.
  • [4] Sharma S. K., Nayak S. (2009) Surface modified clay/polypropylene (PP) nanocomposites: Effect on physico-mechanical, thermal and morphological properties, Polymer Degradation and Stability, 94, 132–138.
  • [5] Colak O. U., Acar A., (2013) Modeling of hydro-thermo-mechanical behavior of Nafion NRE212 for Polymer Electrolyte Membrane Fuel Cells using the Finite Viscoplasticity Theory Based on Overstress for Polymers (FVBOP), Mech. Time-Dependent Materials, 17:331–347
  • [6] Richeton J., Ahzi S., Vecchio k.S., Jiang F.C., Makradi, A. (2007) Modeling and validation of the large deformation inelastic response of amorphous polymers over a wide range of temperatures and strain rates, International Journal of Solids and Structures 44, 7938–7954.
  • [7] Sekercioglu T., Canyurt O. E. (2013) Development of the positive mean stress diagrams using genetic algorithm approach, Fatigue & Fracture of Engineering Materials & Structures, 37, 306-313.
  • [8] Wardeh M. and Toutanji H. A. (2015) Parameter estimation of an anisotropic damage model for concrete using genetic algorithms, International Journal of Damage Mechanics, 1–25.
  • [9] Guo Y., ·Meng G., Li, H. (2009) Parameter determination and response analysis of viscoelastic material, Arch Appl Mech, 79, 147–155.
  • [10] Dusunceli N., Colak O., Filiz, C. (2010) Determination of material parameters of a viscoplastic model by genetic algorithm, Materials and Design, 31, 1250–1255.
  • [11] Andrade-Campos, A., Thuillier, S., Pilvin, P., Teixeira-Dias, F.., (2007) On the determination of material parameters for internal variable thermoelastic–viscoplastic constitutive models. International Journal of Plasticity, 23, 1349–1379.
  • [12] Reeves, C., Rowe, J. E., (2002) Genetic algorithms: Principles and Perspectives, A Guide to GA Theory, Springer, Boston, MA.
  • [13] Colak O. U., Ahzi S. and Remond Y., (2013) Cooperative viscoplasticity theory based on overstess approach for modeling large deformation behavior of amorphous polymers, Polymer International, 62, 1560-1565.
Toplam 13 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Articles
Yazarlar

Özgen Ümit Çolak Bu kişi benim 0000-0002-4414-3906

Yüksel Çakır Bu kişi benim 0000-0002-4238-8504

Yayımlanma Tarihi 1 Eylül 2020
Gönderilme Tarihi 14 Şubat 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 37 Sayı: 3

Kaynak Göster

Vancouver Çolak ÖÜ, Çakır Y. GENETIC ALGORITHM OPTIMIZATION METHOD FOR PARAMETER ESTIMATION IN THE MODELING OF STORAGE MODULUS OF THERMOPLASTICS. SIGMA. 2020;37(3):981-8.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/