000130663 001__ 130663
000130663 005__ 20240130150402.0
000130663 0247_ $$2doi$$a10.1080/08839514.2019.1646014
000130663 0248_ $$2sideral$$a112966
000130663 037__ $$aART-2019-112966
000130663 041__ $$aeng
000130663 100__ $$0(orcid)0000-0001-9225-0311$$aEfkolidis, Nikolaos
000130663 245__ $$aOptimizing models for sustainable drilling operations using genetic algorithm for the optimum ANN
000130663 260__ $$c2019
000130663 5060_ $$aAccess copy available to the general public$$fUnrestricted
000130663 5203_ $$aIn the present study, Artificial Neural Network (ANN) approaches were adopted for the prediction of thrust force (Fz) and torque (Mz) during drilling of St60 workpiece, according to important cutting parameters such as cutting velocity, feed rate, and cutting tool diameter. During the setup of an ANN, some essential difficulties like the determination of network architecture, the determination of weight coefficients and the selection of training algorithm should be addressed. A combination of genetic algorithm and neural networks (GA-ANN) formulates those difficulties as an optimization problem and resolve it by the help of a suitable optimization method. Finally, a comparison between ANN with network architecture determined by a simple trial and error approach and ANN with architecture determined by a GA-ANN approach is conducted. The comparison of the models showed clearly that adopting genetic algorithm (GA) equals to the improvement of the efficiency of the network performance.
000130663 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttp://creativecommons.org/licenses/by-nc/3.0/es/
000130663 590__ $$a1.172$$b2019
000130663 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b209 / 265 = 0.789$$c2019$$dQ4$$eT3
000130663 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b113 / 136 = 0.831$$c2019$$dQ4$$eT3
000130663 592__ $$a0.317$$b2019
000130663 593__ $$aArtificial Intelligence$$c2019$$dQ3
000130663 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000130663 700__ $$aMarkopoulos, Angelos
000130663 700__ $$aKarkalos, Nikolaos
000130663 700__ $$0(orcid)0000-0002-2729-8957$$aGarcía Hernandez, César$$uUniversidad de Zaragoza
000130663 700__ $$0(orcid)0000-0001-8333-5890$$aHuertas Talon, José Luis$$uUniversidad de Zaragoza
000130663 700__ $$aKyratsis, Panagiotis
000130663 7102_ $$15002$$2305$$aUniversidad de Zaragoza$$bDpto. Ingeniería Diseño Fabri.$$cÁrea Expresión Gráfica en Ing.
000130663 7102_ $$15002$$2515$$aUniversidad de Zaragoza$$bDpto. Ingeniería Diseño Fabri.$$cÁrea Ing. Procesos Fabricación
000130663 773__ $$g33, 10 (2019), 881-901$$pAppl. artif. intell.$$tAPPLIED ARTIFICIAL INTELLIGENCE$$x0883-9514
000130663 8564_ $$s2817249$$uhttps://zaguan.unizar.es/record/130663/files/texto_completo.pdf$$yVersión publicada
000130663 8564_ $$s1158418$$uhttps://zaguan.unizar.es/record/130663/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000130663 909CO $$ooai:zaguan.unizar.es:130663$$particulos$$pdriver
000130663 951__ $$a2024-01-30-14:06:05
000130663 980__ $$aARTICLE