Ferramenta Neural Para Estimação da Condição de Ferramenta na Usinagem de Cerâmicas Avançadas
(Neural Tool Condition Estimation in The Grinding of Advanced Ceramics)
Mauricio Nakai (firstname.lastname@example.org), Hildo Guillardi Junior (email@example.com), Paulo Aguiar (firstname.lastname@example.org), Eduardo Bianchi (email@example.com), Danilo Spatti (firstname.lastname@example.org)
This paper appears in: Revista IEEE América Latina
Publication Date: Jan. 2015
Volume: 13, Issue: 1
Ceramic parts are increasingly replacing metal parts due to their excellent physical, chemical and mechanical properties, however they also make them difficult to manufacture by traditional machining methods. The developments carried out in this work are used to estimate tool wear during the grinding of advanced ceramics. The learning process was fed with data collected from a surface grinding machine with tangential diamond wheel and alumina ceramic test specimens, in three cutting configurations: with depths of cut of 120um, 70um and 20um. The grinding wheel speed was 35m/s and the table speed 2.3m/s. Four neural models were evaluated, namely: Multilayer Perceptron, Radial Basis Function, Generalized Regression Neural Networks and the Adaptive Neuro-Fuzzy Inference System. The models' performance evaluation routines were executed automatically, testing all the possible combinations of inputs, number of neurons, number of layers, and spreading. The computational results reveal that the neural models were highly successful in estimating tool wear, since the errors were lower than 4%.
Ceramic grinding, RBF, GRNN, ANFIS, advanced ceramics
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