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Neural Network Contour Error Predictor in CNC Control Systems

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dc.contributor.author Erwiński, Krystian
dc.contributor.author Paprocki, Marcin
dc.contributor.author Wawrzak, Andrzej
dc.contributor.author Grzesiak, Lech M.
dc.date.accessioned 2016-11-12T15:29:53Z
dc.date.available 2016-11-12T15:29:53Z
dc.date.issued 2016-11-12
dc.identifier.isbn 978-1-5090-1866-6
dc.identifier.other 10.1109/MMAR.2016.7575193
dc.identifier.uri http://repozytorium.umk.pl/handle/item/3911
dc.description Paper presented as poster presentation at MMAR 2016 conference (Międzyzdroje,Poland, 29 Aug.-1 Sept. 2016)
dc.description.abstract This article presents a method for predicting contour error using artificial neural networks. Contour error is defined as the minimum distance between actual position and reference toolpath and is commonly used to measure machining precision of Computerized Numerically Controlled (CNC) machine tools. Offline trained Nonlinear Autoregressive networks with exogenous inputs (NARX) are used to predict following error in each axis. These values and information about toolpath geometry obtained from the interpolator are then used to compute the contour error. The method used for effective off-line training of the dynamic recurrent NARX neural networks is presented. Tests are performed that verify the contour error prediction accuracy using a biaxial CNC machine in a real-time CNC control system. The presented neural network based contour error predictor was used in a predictive feedrate optimization algorithm with constrained contour error.
dc.language.iso eng
dc.relation.ispartofseries 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR);6096
dc.rights info:eu-repo/semantics/openAccess
dc.subject artificial neural network
dc.subject prediction
dc.subject contour error
dc.subject CNC
dc.title Neural Network Contour Error Predictor in CNC Control Systems
dc.type info:eu-repo/semantics/conferenceObject


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