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Modeling Cutting Forces in High-Speed Turning using Artificial Neural Networks

Modelado de las fuerzas de corte en el torneado de alta velocidad utilizando redes neuronales artificiales

dc.creatorHernández-González, Luis W.
dc.creatorCurra-Sosa, Dagnier A.
dc.creatorPérez-Rodríguez, Roberto
dc.creatorZambrano-Robledo, Patricia D.C.
dc.date2021-04-22
dc.date.accessioned2021-08-19T16:21:25Z
dc.date.available2021-08-19T16:21:25Z
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1671
dc.identifier10.22430/22565337.1671
dc.identifier.urihttp://test.repositoriodigital.com:8080/handle/123456789/12068
dc.descriptionCutting forces are very important variables in machining performance because they affect surface roughness, cutting tool life, and energy consumption. Reducing electrical energy consumption in manufacturing processes not only provides economic benefits to manufacturers but also improves their environmental performance. Many factors, such as cutting tool material, cutting speed, and machining time, have an impact on cutting forces and energy consumption. Recently, many studies have investigated the energy consumption of machine tools; however, only a few have examined high-speed turning of plain carbon steel. This paper seeks to analyze the effects of cutting tool materials and cutting speed on cutting forces and Specific Energy Consumption (SEC) during dry high-speed turning of AISI 1045 steel. For this purpose, cutting forces were experimentally measured and compared with estimates of predictive models developed using polynomial regression and artificial neural networks. The resulting models were evaluated based on two performance metrics: coefficient of determination and root mean square error. According to the results, the polynomial models did not reach 70 % in the representation of the variability of the data. The cutting speed and machining time associated with the highest and lowest SEC of CT5015-P10 and GC4225-P25 inserts were calculated. The lowest SEC values of these cutting tools were obtained at a medium cutting speed. Also, the SEC of the GC4225 insert was found to be higher than that of the CT5015 tool.en-US
dc.descriptionLas fuerzas de corte son variables muy importantes para el rendimiento del mecanizado, ya que afectan la rugosidad de la superficie, la vida útil de la herramienta de corte y el consumo de energía. La reducción del consumo de energía eléctrica de los procesos de fabricación no solo beneficia económicamente a los fabricantes, sino que también mejora su comportamiento medioambiental. Muchos factores, como el material de la herramienta de corte, la velocidad de corte y el tiempo de mecanizado, afectan la fuerza de corte y el consumo de energía de la máquina. En la actualidad, muchas investigaciones se han realizado sobre el consumo energético de las máquinas herramienta. Sin embargo, la investigación sobre torneado de acero al carbono a alta velocidad es escasa. En este trabajo se estudiaron los efectos de los materiales de las herramientas de corte y su velocidad sobre las fuerzas de corte y el consumo específico de energía en el torneado en seco de alta velocidad de acero AISI 1045. Las fuerzas de corte se determinaron experimentalmente y se compararon con las estimaciones de los modelos predictivos desarrollados mediante regresión polinomial y redes neuronales artificiales. Los modelos obtenidos fueron evaluados según métricas de desempeño como el coeficiente de determinación y la raíz del error cuadrático medio, donde los modelos polinomiales no superaron el 70% en la representación de la variabilidad de los datos. Se determinó la velocidad de corte y el tiempo de mecanizado relacionados con el mayor y menor consumo de energía de las plaquitas CT5015-P10 y GC4225-P25. Los valores más bajos de consumo de energía de estas herramientas se alcanzaron para la velocidad de corte intermedia. Además, la plaquita GC4225 presentó un mayor consumo que la herramienta CT5015.es-ES
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dc.languageeng
dc.publisherInstituto Tecnológico Metropolitano (ITM)en-US
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1671/1968
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1671/2059
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1671/1969
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1671/1972
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dc.rightsCopyright (c) 2021 TecnoLógicasen-US
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0en-US
dc.sourceTecnoLógicas; Vol. 24 No. 51 (2021); e1671en-US
dc.sourceTecnoLógicas; Vol. 24 Núm. 51 (2021); e1671es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectCutting forcesen-US
dc.subjectSpecific energy consumptionen-US
dc.subjectHigh-speed turningen-US
dc.subjectArtificial neural networksen-US
dc.subjectFuerzas de cortees-ES
dc.subjectConsumo específico de energíaes-ES
dc.subjectTorneado de alta velocidades-ES
dc.subjectRedes Neuronales Artificialeses-ES
dc.titleModeling Cutting Forces in High-Speed Turning using Artificial Neural Networksen-US
dc.titleModelado de las fuerzas de corte en el torneado de alta velocidad utilizando redes neuronales artificialeses-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES


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