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dc.creatorCiro De Filippis, Luigi Alberto
dc.creatorSerio, Livia Maria
dc.creatorFacchini, Francesco
dc.creatorMummolo, Giovanni
dc.date.accessioned2021-01-20T20:31:03Z
dc.date.available2021-01-20T20:31:03Z
dc.identifier.otherhttps://www.intechopen.com/books/advanced-applications-for-artificial-neural-networks/ann-modelling-to-optimize-manufacturing-process
dc.identifier.urihttp://hdl.handle.net/20.500.12010/16803
dc.format.extent27 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherIntechOpenspa
dc.subjectIngeniería civil y generalspa
dc.titleANN Modelling to Optimize Manufacturing Processspa
dc.subject.lembControl y seguimiento de los procesos de fabricaciónspa
dc.subject.lembTecnologías de simulaciónspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.localAbierto (Texto Completo)spa
dc.identifier.doi10.5772/intechopen.71237
dc.relation.referencesLuigi Alberto Ciro De Filippis, Livia Maria Serio, Francesco Facchini and Giovanni Mummolo (December 20th 2017). ANN Modelling to Optimize Manufacturing Process, Advanced Applications for Artificial Neural Networks, Adel El-Shahat, IntechOpen, DOI: 10.5772/intechopen.71237.spa
dc.description.abstractenglishNeural network (NN) model is an efficient and accurate tool for simulating manufacturing processes. Various authors adopted artificial neural networks (ANNs) to optimize multiresponse parameters in manufacturing processes. In most cases the adoption of ANN allows to predict the mechanical proprieties of processed products on the basis of given technological parameters. Therefore the implementation of ANN is hugely beneficial in industrial applications in order to save cost and material resources. In this chapter, following an introduction on the application of the ANN to the manufacturing process, it will be described an important study that has been published on international journals and that has investigated the use of the ANNs for the monitoring, controlling and optimization of the process. Experimental observations were collected in order to train the network and establish numerical relationships between process-related factors and mechanical features of the welded joints. Finally, an evaluation of time-costs parameters of the process, using the control of the ANN model, is conducted in order to identify the costs and the benefits of the prediction model adopted.spa
dc.type.coarhttp://purl.org/coar/resource_type/c_2f33spa
dc.rights.creativecommonshttps://creativecommons.org/licenses/by-nc/4.0/legalcode


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