ANFIS Applied to the Prediction of Surface Roughness in Grinding of Advanced Ceramics

Mauricio E. Nakai, Hildo G. Junior, Marcelo M. Spadotto, Paulo R. Aguiar, and Eduardo C. Bianchi


Grinding, acoustic emission, neural network, surface roughness, cutting power, ANFIS


This paper introduces a methodology for predicting the surface roughness of advanced ceramics using Adaptive Neuro-Fuzzy Inference System (ANFIS). To this end, a grinding machine was used, equipped with an acoustic emission sensor and a power transducer connected to the electric motor rotating the diamond grinding wheel. The alumina workpieces used in this work were pressed and sintered into rectangular bars. Acoustic emission and cutting power signals were collected during the tests and digitally processed to calculate the mean, standard deviation, and two other statistical data. These statistics, as well the root mean square of the acoustic emission and cutting power signals were used as input data for ANFIS. The output values of surface roughness (measured during the tests) were implemented for training and validation of the model. The results indicated that an ANFIS network is an excellent tool when applied to predict the surface roughness of ceramic workpieces in the grinding process.

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