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Open AccessArticle

Memetic Cuckoo-Search-Based Optimization in Machining Galvanized Iron

1
Department of Mechanical Engineering, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Avadi 600 062, India
2
Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majhitar 737 136, India
3
VSB-TU Ostrava, Faculty of Mechanical Engineering, 17. listopadu 2172/15, 708 00 Ostrava, Czech Republic
4
Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majhitar 737 136, India
*
Author to whom correspondence should be addressed.
Materials 2020, 13(14), 3047; https://doi.org/10.3390/ma13143047
Received: 25 May 2020 / Revised: 23 June 2020 / Accepted: 6 July 2020 / Published: 8 July 2020
(This article belongs to the Special Issue Machining and Manufacturing of Alloys and Steels)
In this article, an improved variant of the cuckoo search (CS) algorithm named Coevolutionary Host-Parasite (CHP) is used for maximizing the metal removal rate in a turning process. The spindle speed, feed rate and depth of cut are considered as the independent parameters that describe the metal removal rate during the turning operation. A data-driven second-order polynomial regression approach is used for this purpose. The training dataset is designed using an L16 orthogonal array. The CHP algorithm is effective in quickly locating the global optima. Furthermore, CHP is seen to be sufficiently robust in the sense that it is able to identify the optima on independent reruns. The CHP predicted optimal solution presents ±10% deviations in the optimal process parameters, which shows the robustness of the optimal solution. View Full-Text
Keywords: regression analysis; material removal rate (MRR); cuckoo search; optimization regression analysis; material removal rate (MRR); cuckoo search; optimization
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MDPI and ACS Style

Kalita, K.; Ghadai, R.K.; Cepova, L.; Shivakoti, I.; Bhoi, A.K. Memetic Cuckoo-Search-Based Optimization in Machining Galvanized Iron. Materials 2020, 13, 3047.

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