Neural network approach for surface roughness prediction in surface grinding

• Autor(a) principal: Aguiar, Paulo R. [UNESP]
• Data de Publicação: 2007
• Outros Autores: Cruz, Carlos E. D. [UNESP], Paula, Wallace C. F. [UNESP], Bianchi, Eduardo C. [UNESP], Thomazella, Rogério [UNESP], Dotto, Fábio R. L. [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://www.actapress.com/Abstract.aspx?paperId=29434; http://hdl.handle.net/11449/70158
• topic: Acoustic emission; Electric power; Neural network; Surface finishing; Surface grinding; Surface roughness; Acoustic emission testing; Electric power systems; Finishing; Grinding (machining); Neural networks

Several systems are currently tested in order to obtain a feasible and safe method for automation and control of grinding process. This work aims to predict the surface roughness of the parts of SAE 1020 steel ground in a surface grinding machine. Acoustic emission and electrical power signals were acquired by a commercial data acquisition system. The former from a fixed sensor placed near the workpiece and the latter from the electric induction motor that drives the grinding wheel. Both signals were digitally processed through known statistics, which with the depth of cut composed three data sets implemented to the artificial neural networks. The neural network through its mathematical logical system interpreted the signals and successful predicted the workpiece roughness. The results from the neural networks were compared to the roughness values taken from the worpieces, showing high efficiency and applicability on monitoring and controlling the grinding process. Also, a comparison among the three data sets was carried out.