2018
DOI: 10.1088/1757-899x/324/1/012076
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Real-time parameter optimization based on neural network for smart injection molding

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Cited by 12 publications
(5 citation statements)
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“…Although public health emergency management has been paid much attention, the research in this area is still in its infancy and initial stage. Especially in the "top down" emergency response system, how to highlight the role and function of primary medical units is a problem that we are waiting to study at present, especially the remote public health emergency management of the Internet of things is also in the exploratory stage [27][28][29]. is paper will focus on the current situation and focus on analyzing the low delay emergency management system of remote public medical care based on the Internet of things, which has a strong convincing and promoting role in improving the relevant mechanism.…”
Section: Introductionmentioning
confidence: 99%
“…Although public health emergency management has been paid much attention, the research in this area is still in its infancy and initial stage. Especially in the "top down" emergency response system, how to highlight the role and function of primary medical units is a problem that we are waiting to study at present, especially the remote public health emergency management of the Internet of things is also in the exploratory stage [27][28][29]. is paper will focus on the current situation and focus on analyzing the low delay emergency management system of remote public medical care based on the Internet of things, which has a strong convincing and promoting role in improving the relevant mechanism.…”
Section: Introductionmentioning
confidence: 99%
“…Many more researchers proposed ANN-based models of the injection molding process for a subsequent optimization of the product warpage [17][18][19][20][21][22], mechanical properties [23][24][25], or even a combination of several quality parameters together in a single model [26][27][28]. Each of the above described research works refer to an explicitly generated database, introducing an iterative data generation process and therefore costs into the optimization.…”
Section: Artificial Neural Network In Injection Moldingmentioning
confidence: 99%
“…Dado que esto es foco de interés, se han planteado a lo largo del tiempo diferentes soluciones para controlar estas variables. Resumiendo los resultados de estas implementaciones podemos ver que en (Zhang, S., Dubay, R., & Charest, M., 2014), (Tan, Y. S., Ng, Y. T., & Low, J. S. C., 2017), (Vrabič, R., Kozjek, D., & Butala, P., 2017), (Hu, F., He, Z., Zhao, X., & Zhang, S., 2017), (Siller, H. R., Romero, D., Rabelo, R. J., & Vazquez, E., 2018), (Ogorodnyk, O., & Martinsen, K., 2018), (Lucchetta, G., Masato, D., & Sorgato, M., 2018), (Ageyeva, T., Horváth, S., & Kovács, J. G., 2019), (Park, H. S., Phuong, D. X., & Kumar, S., 2019, (Lee, H., Liau, Y., & Ryu, K., 2018) y (Charest, M., Finn, R., & Dubay, R., 2018) se utiliza tecnología de sensores de temperatura y presiones en las cavidades de los moldes; cada una de las implementaciones ha tomado diferentes medidas en acondicionar las herramientas y las máquinas para poder recopilar los datos, mientras que en los casos donde se ha realizado integración de tecnología, se ha buscado que esta sea la adecuada para obtener los resultados deseados, sobre todo en (Ageyeva, T., Horváth, S., & Kovács, J. G., 2019) donde se proponen diferentes maneras de obtener los datos del comportamiento de plástico por medio de tecnologías diversas.…”
Section: Datos Medidas Y Casos De Aplicaciónunclassified