Thermal Behavior Modeling Based on BP Neural Network in Keras Framework for Motorized Machine Tool Spindles

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dc.citation.spage7782
dc.citation.volume15
dc.contributor.authorKosarac, Aleksandar
dc.contributor.authorCep, Robert
dc.contributor.authorTrochta, Miroslav
dc.contributor.authorKnezev, Milos
dc.contributor.authorZivkovic, Aleksandar
dc.contributor.authorMladjenovic, Cvijetin
dc.contributor.authorAntic, Aco
dc.date.accessioned2023-05-05T11:50:56Z
dc.date.available2023-05-05T11:50:56Z
dc.date.issued2022
dc.description.abstractThis paper presents the development and evaluation of neural network models using a small input–output dataset to predict the thermal behavior of a high-speed motorized spindles. Different neural multi-output regression models were developed and evaluated using Keras, one of the most popular deep learning frameworks at the moment. ANN was developed and evaluated considering the following: the influence of the topology (number of hidden layers and neurons within), the learning parameter, and validation techniques. The neural network was simulated using a dataset that was completely unknown to the network. The ANN model was used for analyzing the effect of working conditions on the thermal behavior of the motorized grinder spindle. The prediction accuracy of the ANN model for the spindle thermal behavior ranged from 95% to 98%. The results show that the ANN model with small datasets can accurately predict the temperature of the spindle under different working conditions. In addition, the analysis showed a very strong effect of type coolant on spindle unit temperature, particularly for intensive cooling with water
dc.identifier.doi10.3390/ma15217782
dc.identifier.urihttps://vaseljena.ues.rs.ba/handle/123456789/113
dc.language.isoen
dc.publisherMDPI
dc.sourceMaterials
dc.subjecthigh-speed motorized spindle; thermal behavior; deep learning; neural network; small dataset; Keras; TensorFlow
dc.titleThermal Behavior Modeling Based on BP Neural Network in Keras Framework for Motorized Machine Tool Spindles
dc.typeArticle
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Машински факултет [Научни радови] / Faculty of Mechanical Engineering [Scientific papers]