Optimizing models for sustainable drilling operations using genetic algorithm for the optimum ANN
Efkolidis, Nikolaos ; Markopoulos, Angelos ; Karkalos, Nikolaos ; García Hernandez, César (Universidad de Zaragoza) ; Huertas Talon, José Luis (Universidad de Zaragoza) ; Kyratsis, Panagiotis
Resumen: 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.
Idioma: Inglés
DOI: 10.1080/08839514.2019.1646014
Año: 2019
Publicado en: APPLIED ARTIFICIAL INTELLIGENCE 33, 10 (2019), 881-901
ISSN: 0883-9514
Factor impacto JCR: 1.172 (2019)
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 209 / 265 = 0.789 (2019) - Q4 - T3
Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 113 / 136 = 0.831 (2019) - Q4 - T3
Factor impacto SCIMAGO: 0.317 - Artificial Intelligence (Q3)
Tipo y forma: Article (Published version)
Área (Departamento): Área Expresión Gráfica en Ing. (Dpto. Ingeniería Diseño Fabri.)
Área (Departamento): Área Ing. Procesos Fabricación (Dpto. Ingeniería Diseño Fabri.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes.
Exportado de SIDERAL (2024-01-30-14:06:05)
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Idioma: Inglés
DOI: 10.1080/08839514.2019.1646014
Año: 2019
Publicado en: APPLIED ARTIFICIAL INTELLIGENCE 33, 10 (2019), 881-901
ISSN: 0883-9514
Factor impacto JCR: 1.172 (2019)
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 209 / 265 = 0.789 (2019) - Q4 - T3
Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 113 / 136 = 0.831 (2019) - Q4 - T3
Factor impacto SCIMAGO: 0.317 - Artificial Intelligence (Q3)
Tipo y forma: Article (Published version)
Área (Departamento): Área Expresión Gráfica en Ing. (Dpto. Ingeniería Diseño Fabri.)
Área (Departamento): Área Ing. Procesos Fabricación (Dpto. Ingeniería Diseño Fabri.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. Exportado de SIDERAL (2024-01-30-14:06:05)
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Este artículo se encuentra en las siguientes colecciones:
Articles > Artículos por área > Ingeniería de los Procesos de Fabricación
Articles > Artículos por área > Expresión Gráfica de la Ingeniería
Record created 2024-01-30, last modified 2024-01-30