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.
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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.
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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. 6, 1–19 (2023). https://doi.org/10.1007/s41939-022-00126-9
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DOI: https://doi.org/10.1007/s41939-022-00126-9