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Memetic Cuckoo-Search-Based Optimization in Machining Galvanized Iron
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 rem...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7411800/ https://www.ncbi.nlm.nih.gov/pubmed/32650437 http://dx.doi.org/10.3390/ma13143047 |
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author | Kalita, Kanak Ghadai, Ranjan Kumar Cepova, Lenka Shivakoti, Ishwer Bhoi, Akash Kumar |
author_facet | Kalita, Kanak Ghadai, Ranjan Kumar Cepova, Lenka Shivakoti, Ishwer Bhoi, Akash Kumar |
author_sort | Kalita, Kanak |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7411800 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74118002020-08-25 Memetic Cuckoo-Search-Based Optimization in Machining Galvanized Iron Kalita, Kanak Ghadai, Ranjan Kumar Cepova, Lenka Shivakoti, Ishwer Bhoi, Akash Kumar Materials (Basel) Article 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. MDPI 2020-07-08 /pmc/articles/PMC7411800/ /pubmed/32650437 http://dx.doi.org/10.3390/ma13143047 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kalita, Kanak Ghadai, Ranjan Kumar Cepova, Lenka Shivakoti, Ishwer Bhoi, Akash Kumar Memetic Cuckoo-Search-Based Optimization in Machining Galvanized Iron |
title | Memetic Cuckoo-Search-Based Optimization in Machining Galvanized Iron |
title_full | Memetic Cuckoo-Search-Based Optimization in Machining Galvanized Iron |
title_fullStr | Memetic Cuckoo-Search-Based Optimization in Machining Galvanized Iron |
title_full_unstemmed | Memetic Cuckoo-Search-Based Optimization in Machining Galvanized Iron |
title_short | Memetic Cuckoo-Search-Based Optimization in Machining Galvanized Iron |
title_sort | memetic cuckoo-search-based optimization in machining galvanized iron |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7411800/ https://www.ncbi.nlm.nih.gov/pubmed/32650437 http://dx.doi.org/10.3390/ma13143047 |
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