Optimizing Models for Sustainable Drilling Operations Using Genetic Algorithm for the Optimum ANN

Autor: Nikolaos Efkolidis, Angelos Markopoulos, Nikolaos Karkalos, César García Hernández, José Luis Huertas Talón, Panagiotis Kyratsis
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Applied Artificial Intelligence, Vol 33, Iss 10, Pp 881-901 (2019)
Druh dokumentu: article
ISSN: 0883-9514
1087-6545
08839514
DOI: 10.1080/08839514.2019.1646014
Popis: 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.
Databáze: Directory of Open Access Journals
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