The Multi-objective Optimization Design Approach for Carbon Fiber Hybrid Nanocomposites Containing NanoClay and NanoZnO Particles by Using OptiComp

Abstract

This paper presents the multi-objective design optimization of Carbon Fiber-Reinforced Polymer hybrid nanocomposites by OptiComp. The use of nanoparticles not only improves the interface strength but also significantly improves stress and hence the propagation of strain in composite laminates, which ultimately improves the stiffness of the composite laminate. The OptiComp is the comprehensive generic procedure for design optimization of the composites. With the utilization of OptiComp, the discrete form Variable thickness approach (VTA) used in this study can discover the minimum laminate thickness, and for maximum stiffness in one step instead of two-step method. The Max stress theory and Tsai-Wu theory as constraints for the current study in optimization are performed using a direct value coding genetic algorithm. It is found that the CFRP nanocomposites up to 2 to 3 wt% range of nanoparticles can be designed for max. stiffness and min. weight.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Abbreviations

E 11 :

Longitudinal direction elastic modulus

E22:

Transverse directional elastic modulus

G 12 :

Shear modulus in plane

n 12 :

Major poisson’s ratio

S lc :

Longitudinal directional compressive strength

S lt :

Longitudinal directional tensile strength

S tc :

Transverse directional compressive strength

S tt :

Transverse directional tensile strength

S lts :

Shear strength

ρ :

Mass density

a :

Length of plate

b :

Width of plate

T:

Total Thickness of plate

References

  1. 1.

    Mohammadi D et al (2015) A multilevel approach for analysis and optimization of nano-enhanced composite structures. Compos Struc 131:1050–1059

    Article  Google Scholar 

  2. 2.

    Taha MR et al (2009) A multi-objective optimization approach for the design of blast-resistantcomposite laminates using carbon nanotubes. Composites B 40:522–529

    Article  Google Scholar 

  3. 3.

    Zhu X et al (2015) An optimization technique for the composite strut using Genetic Algorithms. Mater Des 65:482–488

    CAS  Article  Google Scholar 

  4. 4.

    Alemi-Ardakani M et al (2015) A rapid approach for prediction and discrete lay-up optimization of glass fiber/polypropylene composite laminates under the impact. Int J Impact Eng 84:34–144

    Article  Google Scholar 

  5. 5.

    Paluch B et al (2008) Combining a finite element program and a genetic algorithm to optimize composite structures with variable thickness. Compos Struct 83:284–294

    Article  Google Scholar 

  6. 6.

    Almeida F et al (2009) Design optimization of composite laminated structures using genetic algorithms and finite element analysis. Compos Struct 88:443–454

    Article  Google Scholar 

  7. 7.

    Kim D-H et al (2015) Design optimization and manufacture of hybrid glass/carbon fiber reinforced composite bumper beam for automobile vehicle. Compos Struct 131:742–752

    Article  Google Scholar 

  8. 8.

    Kim D-H et al (2014) Design optimization of a carbon fiber reinforced composite automotive lower arm. Composites B 58:400–407

    CAS  Article  Google Scholar 

  9. 9.

    Nelson S et al (2016) (2016) Composite laminate failure parameter optimization through four points flexure experimentation and analysis. Composites B 97:92–102

    CAS  Article  Google Scholar 

  10. 10.

    Naik N et al (2008) Design optimization of composites using genetic algorithms and failure mechanism based failure criterion. Compos Struct 83:354–367

    Article  Google Scholar 

  11. 11.

    Sohouli M et al (2017) Design optimization of thin-walled composite structures based on material and fiber orientation. Compos Struct. https://doi.org/10.1016/j.compstruct.2017.06.030

    Article  Google Scholar 

  12. 12.

    Rostamiyan Y et al (2015) Experimental and optimizing flexural strength of epoxy-based nanocomposite: effect of using nano-silica and nano clay by using response surface design methodology. Mater Des 69:96–104

    CAS  Article  Google Scholar 

  13. 13.

    Rostamiyan Y et al (2015) Experimental, modeling, and optimization study on the mechanical properties of epoxy/high impact polystyrene/multi-walled carbon nanotubes ternary nanocomposite using artificial neural network and genetic algorithm. Mater Des 65:1236–1244

    CAS  Article  Google Scholar 

  14. 14.

    Rostamiyan Y et al (2015) Using response surface methodology for modeling and optimizing tensile and impact strength properties of fiber orientated quaternary hybrid nanocomposite. Composites B 69:304–316

    CAS  Article  Google Scholar 

  15. 15.

    Chang BP et al (2014) Comparative study of wear performance of particulate and fiber-reinforced nano-ZnO/ultra-high molecular weight polyethylene hybrid composites using response surface methodology. Mater Des 63:805–819

    CAS  Article  Google Scholar 

  16. 16.

    Boroujeni AY et al (2014) Hybrid carbon nanotube–carbon fiber composites with improved in-plane mechanical properties. Composites B 66:475–483

    CAS  Article  Google Scholar 

  17. 17.

    Mirmohseni A et al (2011) Modelling and optimization of a new impact-toughened epoxy nanocomposite using response surface methodology. Polym Res 18:509–517

    CAS  Article  Google Scholar 

  18. 18.

    Shrivastava S et al (2018) Multi-objective multi-laminate design and optimization of a carbon fibre composite wing torsion box using evolutionary algorithm. Compos Struct 185:132–147

    Article  Google Scholar 

  19. 19.

    MarianN. Velea. et al (2014) Multi-objective optimization of vehicle bodies made of FRP sandwich structures. Compos Struct 111:75–84

    Article  Google Scholar 

  20. 20.

    Shojaeefard et al (2014) Multi-objective Optimization of a CNT/Polymer Nanocomposite Automotive Drive Shaft. In: The 3rd International Conference on Design Engineering and Science, ICDES 2014 Pilsen, Czech Republic, August 31–September 3, pp 92–99.

  21. 21.

    Pelletier J (2006) Multi-objective optimization of fiber reinforced composite laminates for strength, stiffness and minimal mass. Comput Struct 84:2065–2080

    Article  Google Scholar 

  22. 22.

    Kalantari M et al (2016) Multi-objective robust optimization of unidirectional carbon/glass fibre reinforced hybrid composites under flexural loading. Compos Struct 138:264–275

    Article  Google Scholar 

  23. 23.

    Irisarr FX et al (2009) Multiobjective stacking sequence optimization for laminated composite structures. Compos Sci Technol 69:983–990

    Article  Google Scholar 

  24. 24.

    De Munck M et al (2015) Multi-objective weight and cost optimization of hybrid composite-concrete beams. Compos Struct 134:369–377

    Article  Google Scholar 

  25. 25.

    Naik N et al (2011) Nature-inspired optimization techniques for the design optimization of laminated composite structures using failure criteria. Exp Syst Appl 38:2489–2499

    Article  Google Scholar 

  26. 26.

    Rahul D et al (2005) Optimization of FRP composites against impact-induced failure using island model parallel genetic algorithm. Compos Sci Technol 65:2003–2013

    CAS  Article  Google Scholar 

  27. 27.

    Lopez RH et al (2009) Optimization of laminated composites considering different failure criteria. Composites B 40:731–740

    Article  Google Scholar 

  28. 28.

    Chow WS et al (2008) Optimization of process variables on flexural properties of epoxy/organo-montmorillonite nanocomposite by response surface methodology. Exp Polym Lett 2(1):2–11

    CAS  Article  Google Scholar 

  29. 29.

    Nik MA et al (2014) Optimization of variable stiffness composites with embedded defects induced by automated fiber placement. Compos Struct 107:160–166

    Article  Google Scholar 

  30. 30.

    Fe J et al (2018) Optimizing fiber/matrix interface by growth MnO nanosheets for achieving desirable mechanical and tribological properties. Appl Surf Sci 452:364–371

    Article  Google Scholar 

  31. 31.

    Balachandran M et al (2012) Optimizing properties of nanoclay–nitrile rubber (NBR) composites using Face Centred Central Composite Design. Mater Des 35:854–862

    CAS  Article  Google Scholar 

  32. 32.

    Abilash et al (2016) Optimizing the delamination failure in bamboo fiber reinforced polyester composite. J King Saud Univ 28:92–102

  33. 33.

    Erdal O et al (2005) Optimum design of composite laminates for maximum buckling load capacity using simulated annealing. Compos Struct 71:45–52

    Article  Google Scholar 

  34. 34.

    Akbulut M et al (2008) Optimum design of composite laminates for minimum thickness. Comput Struct 86:1974–1982

    Article  Google Scholar 

  35. 35.

    Park CH et al (2004) Simultaneous optimization of composite structures considering mechanical performance and manufacturing cost. Compos Struct 65:117–127

    Article  Google Scholar 

  36. 36.

    Srinivas V (2020) Effect of ultrasonic stir casting technique on mechanical and tribological properties of aluminium–multi-walled carbon nanotube nanocomposites. J Bio Tribo corrosion article no. 30, Feb 2020

  37. 37.

    Tripathi VK et al (2019) Manufacturing cost-effective weight minimization of composite laminate using uniform thickness and variable thickness approaches considering different failure criteria. Compos Struct. https://doi.org/10.1007/s41939-019-00048

    Article  Google Scholar 

  38. 38.

    Kulkarni N, Tripathi VK (2019) Buckling load maximization of composite laminate using a random search algorithm considering the uniform thickness and variable thickness approach. J Eng Sci Technol 14(3):1330–1343

    Google Scholar 

  39. 39.

    Kulkarni N, Tripathi VK (2018) Variable thickness approach for finding minimum laminate thickness and investigating the effect of different design variables on its performance. Arch Mech Eng. https://doi.org/10.24425/ame.2018.125441

    Article  Google Scholar 

  40. 40.

    Vo-Duy T et al (2016) A global numerical approach for lightweight design optimization of laminated composite plates subjected to frequency constraints. Compos Struct. https://doi.org/10.1016/j.compstruct.2016.09.059

    Article  Google Scholar 

  41. 41.

    Fakhri LA et al (2018) Optimization of mechanical and color properties of polystyrene/nanoclay/nanoZnO based nanocomposite packaging sheet using response surface methodology. Food Packag Shelf Life 17:11–24

    Article  Google Scholar 

  42. 42.

    Balaji L et al (2020) Study on mechanical, thermal and morphological properties of banana fiber-reinforced epoxy composites. J Bio Tribo corrosion article no. 60, April 2020

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Shailesh D. Ambekar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ambekar, S.D., Tripathi, V.K. The Multi-objective Optimization Design Approach for Carbon Fiber Hybrid Nanocomposites Containing NanoClay and NanoZnO Particles by Using OptiComp. J Bio Tribo Corros 6, 90 (2020). https://doi.org/10.1007/s40735-020-00386-3

Download citation

Keywords

  • OptiComp
  • VTA
  • Nanocomposite
  • CFRP
  • Nanoclay
  • NanoZnO