Anfis applied to the prediction of surface roughness in grinding of advanced ceramics

• Autor(a) principal: Nakai, Mauricio E. [UNESP]
• Data de Publicação: 2011
• Outros Autores: Guillardi Júnior, Hildo [UNESP], Spadotto, Marcelo M. [UNESP], Aguiar, Paulo R. [UNESP], Bianchi, Eduardo C. [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.2316/P.2011.716-005; http://hdl.handle.net/11449/72896
• topic: Acoustic emission; ANFIS; Cutting power; Grinding; Neural network; Surface roughness; Acoustic emission sensors; Adaptive neuro-fuzzy inference system; Diamond grinding wheel; Power transducers; Standard deviation; Statistical datas; Acoustic emission testing; Acoustic emissions; Artificial intelligence; Ceramic materials; Forecasting; Grinding (machining); Neural networks; Sintered alumina; Sintering; Soft computing

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.