130663 20240130150402.0 doi 10.1080/08839514.2019.1646014 sideral 112966 ART-2019-112966 eng Efkolidis, Nikolaos (orcid)0000-0001-9225-0311 Optimizing models for sustainable drilling operations using genetic algorithm for the optimum ANN 2019 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. Access copy available to the general public Unrestricted info:eu-repo/semantics/openAccess by-nc http://creativecommons.org/licenses/by-nc/3.0/es/ 1.172 2019 ENGINEERING, ELECTRICAL & ELECTRONIC 209 / 265 = 0.789 2019 Q4 T3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 113 / 136 = 0.831 2019 Q4 T3 0.317 2019 Artificial Intelligence 2019 Q3 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Markopoulos, Angelos Karkalos, Nikolaos García Hernandez, César Universidad de Zaragoza (orcid)0000-0002-2729-8957 Huertas Talon, José Luis Universidad de Zaragoza (orcid)0000-0001-8333-5890 Kyratsis, Panagiotis 5002 305 Universidad de Zaragoza Dpto. Ingeniería Diseño Fabri. Área Expresión Gráfica en Ing. 5002 515 Universidad de Zaragoza Dpto. Ingeniería Diseño Fabri. Área Ing. Procesos Fabricación 33, 10 (2019), 881-901 Appl. artif. intell. APPLIED ARTIFICIAL INTELLIGENCE 0883-9514 2817249 http://zaguan.unizar.es/record/130663/files/texto_completo.pdf Versión publicada 1158418 http://zaguan.unizar.es/record/130663/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:130663 articulos driver 2024-01-30-14:06:05 ARTICLE