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Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing
Recently, the concept of smart manufacturing systems urges for intelligent optimization of process parameters to eliminate wastage of resources, especially materials and energy. In this context, the current study deals with optimization of hard-turning parameters using evolutionary algorithms. Thoug...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6471085/ https://www.ncbi.nlm.nih.gov/pubmed/30875993 http://dx.doi.org/10.3390/ma12060879 |
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author | Mia, Mozammel Królczyk, Grzegorz Maruda, Radosław Wojciechowski, Szymon |
author_facet | Mia, Mozammel Królczyk, Grzegorz Maruda, Radosław Wojciechowski, Szymon |
author_sort | Mia, Mozammel |
collection | PubMed |
description | Recently, the concept of smart manufacturing systems urges for intelligent optimization of process parameters to eliminate wastage of resources, especially materials and energy. In this context, the current study deals with optimization of hard-turning parameters using evolutionary algorithms. Though the complex programming, parameters selection, and ability to obtain the global optimal solution are major concerns of evolutionary based algorithms, in the present paper, the optimization was performed by using efficient algorithms i.e., teaching–learning-based optimization and bacterial foraging optimization. Furthermore, the weighted sum method was used to transform the diverse responses into a single response, and then multi-objective optimization was performed using the teaching–learning-based optimization method and the standard bacterial foraging optimization method. Finally, the optimum results reported by these methods are compared to choose the best method. In fact, owing to better convergence within shortest time, the teaching–learning-based optimization approach is recommended. It is expected that the outcome of this research would help to efficiently and intelligently perform the hard-turning process under automatic and optimized environment. |
format | Online Article Text |
id | pubmed-6471085 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64710852019-04-27 Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing Mia, Mozammel Królczyk, Grzegorz Maruda, Radosław Wojciechowski, Szymon Materials (Basel) Article Recently, the concept of smart manufacturing systems urges for intelligent optimization of process parameters to eliminate wastage of resources, especially materials and energy. In this context, the current study deals with optimization of hard-turning parameters using evolutionary algorithms. Though the complex programming, parameters selection, and ability to obtain the global optimal solution are major concerns of evolutionary based algorithms, in the present paper, the optimization was performed by using efficient algorithms i.e., teaching–learning-based optimization and bacterial foraging optimization. Furthermore, the weighted sum method was used to transform the diverse responses into a single response, and then multi-objective optimization was performed using the teaching–learning-based optimization method and the standard bacterial foraging optimization method. Finally, the optimum results reported by these methods are compared to choose the best method. In fact, owing to better convergence within shortest time, the teaching–learning-based optimization approach is recommended. It is expected that the outcome of this research would help to efficiently and intelligently perform the hard-turning process under automatic and optimized environment. MDPI 2019-03-15 /pmc/articles/PMC6471085/ /pubmed/30875993 http://dx.doi.org/10.3390/ma12060879 Text en © 2019 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 Mia, Mozammel Królczyk, Grzegorz Maruda, Radosław Wojciechowski, Szymon Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing |
title | Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing |
title_full | Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing |
title_fullStr | Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing |
title_full_unstemmed | Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing |
title_short | Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing |
title_sort | intelligent optimization of hard-turning parameters using evolutionary algorithms for smart manufacturing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6471085/ https://www.ncbi.nlm.nih.gov/pubmed/30875993 http://dx.doi.org/10.3390/ma12060879 |
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