Optimizing Models for Sustainable Drilling Operations Using Genetic Algorithm for the Optimum ANN
In the present study, Artificial Neural Network (ANN) approaches were adopted for the prediction of thrust force (Fz) and torque (Mz) during drilling of St60 workpiece, according to important cutting parameters such as cutting velocity, feed rate, and cutting tool diameter. During the setup of an AN...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2019-08-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2019.1646014 |
| _version_ | 1797684878623375360 |
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| author | Nikolaos Efkolidis Angelos Markopoulos Nikolaos Karkalos César García Hernández José Luis Huertas Talón Panagiotis Kyratsis |
| author_facet | Nikolaos Efkolidis Angelos Markopoulos Nikolaos Karkalos César García Hernández José Luis Huertas Talón Panagiotis Kyratsis |
| author_sort | Nikolaos Efkolidis |
| collection | DOAJ |
| description | In the present study, Artificial Neural Network (ANN) approaches were adopted for the prediction of thrust force (Fz) and torque (Mz) during drilling of St60 workpiece, according to important cutting parameters such as cutting velocity, feed rate, and cutting tool diameter. During the setup of an ANN, some essential difficulties like the determination of network architecture, the determination of weight coefficients and the selection of training algorithm should be addressed. A combination of genetic algorithm and neural networks (GA-ANN) formulates those difficulties as an optimization problem and resolve it by the help of a suitable optimization method. Finally, a comparison between ANN with network architecture determined by a simple trial and error approach and ANN with architecture determined by a GA-ANN approach is conducted. The comparison of the models showed clearly that adopting genetic algorithm (GA) equals to the improvement of the efficiency of the network performance. |
| first_indexed | 2024-03-12T00:36:07Z |
| format | Article |
| id | doaj.art-6308da5c240d48eaa9a0d5634103f0fb |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:36:07Z |
| publishDate | 2019-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-6308da5c240d48eaa9a0d5634103f0fb2023-09-15T09:33:57ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452019-08-01331088190110.1080/08839514.2019.16460141646014Optimizing Models for Sustainable Drilling Operations Using Genetic Algorithm for the Optimum ANNNikolaos Efkolidis0Angelos Markopoulos1Nikolaos Karkalos2César García Hernández3José Luis Huertas Talón4Panagiotis Kyratsis5University of ZaragozaNational Technical University of AthensNational Technical University of AthensUniversity of ZaragozaUniversity of ZaragozaWestern Macedonia University of Applied SciencesIn the present study, Artificial Neural Network (ANN) approaches were adopted for the prediction of thrust force (Fz) and torque (Mz) during drilling of St60 workpiece, according to important cutting parameters such as cutting velocity, feed rate, and cutting tool diameter. During the setup of an ANN, some essential difficulties like the determination of network architecture, the determination of weight coefficients and the selection of training algorithm should be addressed. A combination of genetic algorithm and neural networks (GA-ANN) formulates those difficulties as an optimization problem and resolve it by the help of a suitable optimization method. Finally, a comparison between ANN with network architecture determined by a simple trial and error approach and ANN with architecture determined by a GA-ANN approach is conducted. The comparison of the models showed clearly that adopting genetic algorithm (GA) equals to the improvement of the efficiency of the network performance.http://dx.doi.org/10.1080/08839514.2019.1646014 |
| spellingShingle | Nikolaos Efkolidis Angelos Markopoulos Nikolaos Karkalos César García Hernández José Luis Huertas Talón Panagiotis Kyratsis Optimizing Models for Sustainable Drilling Operations Using Genetic Algorithm for the Optimum ANN Applied Artificial Intelligence |
| title | Optimizing Models for Sustainable Drilling Operations Using Genetic Algorithm for the Optimum ANN |
| title_full | Optimizing Models for Sustainable Drilling Operations Using Genetic Algorithm for the Optimum ANN |
| title_fullStr | Optimizing Models for Sustainable Drilling Operations Using Genetic Algorithm for the Optimum ANN |
| title_full_unstemmed | Optimizing Models for Sustainable Drilling Operations Using Genetic Algorithm for the Optimum ANN |
| title_short | Optimizing Models for Sustainable Drilling Operations Using Genetic Algorithm for the Optimum ANN |
| title_sort | optimizing models for sustainable drilling operations using genetic algorithm for the optimum ann |
| url | http://dx.doi.org/10.1080/08839514.2019.1646014 |
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