Skip to main content

Nature-inspired grasshopper optimization algorithm (GOA) for predictive modeling and machinability evaluation of laminated polymer nanocomposites

Abstract

Polymeric nanocomposites are broadly used in the manufacturing of aircraft, space, optical, and biomedical components. It is mainly due to their exceptional physiomechanical performances. The carbon nanotube (CNTs) reinforced fibrous polymer composite significantly replaced the heavyweight metallic materials and their alloys. This work examines the Milling performances of polymer nanocomposite modified by Glass fiber and Multiwall carbon nanotube (MWCNT). The considered varying constraints, namely, Spindle speed (S), Feed rate (F), Depth of Cut (D), and weight % MWCNT (wt.%), have effectively controlled to acquire the desired value. The experimentation was planned according to Taguchi L9 Orthogonal array (OA). The aggregation of responses, namely, Material removal rate (MRR), Cutting force (Fc), and Surface roughness (Ra), was achieved by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The optimal combination of machining constraints is achieved using the Grasshopper optimization algorithm (GOA). GOA mitigates the bionic-inspired principles to search for the best target among the many solutions. The main feature of the GOA is the larval phase is slow drive and minor steps of the Grasshopper. This makes it unique from other algorithms in obtaining a global solution. The Scanning electron microscopy (SEM) results show the feasible machined surface and higher potential of the proposed algorithm in the manufacturing environment.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Availability of data and materials

Data will be made available on reasonable request.

References

Download references

Acknowledgements

The authors would like to acknowledge the kind support of the Indian Institute of Technology Roorkee (Saharanpur Campus), India, to extend all possible testing and characterization facilities in carrying out this research work.

Funding

This research work is financially supported by the Ministry of Textiles, Govt. of INDIA, under the R&D scheme Project ID: K-12012/4/19/2020-21/R&D/ST.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rajesh Kumar Verma.

Ethics declarations

Conflicts of interest

The authors declared no potential conflicts of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kumar, K., Verma, R.K. Nature-inspired grasshopper optimization algorithm (GOA) for predictive modeling and machinability evaluation of laminated polymer nanocomposites. Multiscale and Multidiscip. Model. Exp. and Des. (2022). https://doi.org/10.1007/s41939-022-00126-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41939-022-00126-9

Keywords

  • MWCNT
  • Polymer
  • Milling
  • Algorithm
  • Grasshopper