Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics

Author:

Nakai Mauricio Eiji,Aguiar Paulo Roberto,Guillardi Hildo,Bianchi Eduardo Carlos,Spatti Danilo Hernane,D’Addona Doriana Marilena

Funder

CNPq

CAPES

Brazilian Federal Research Agencies

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference47 articles.

1. Experimental investigation of surface/subsurface damage formation and material removal mechanisms in SiC grinding;Agarwal;International Journal of Machine Tools and Manufacture,2008

2. Predictive modeling of force and power based on a new analytical undeformed chip thickness model in ceramic grinding;Agarwal;International Journal of Machine Tools and Manufacture,2013

3. A method for burning detection in grinding process using acoustic emission and effective electric power signals;Aguiar;Manufacturing Systems,2002

4. Study of thresholds to burning in surface grinding process;Aguiar;Journal of the Brazilian Society of Mechanical Science & Engineering,2005

5. Aguiar, P. R., Cruz, C. E. D., Paula, W. C. F., Bianchi, E. C., Thomazella, R., & Dotto, F. R. L. (2007). Neural network approach for surface roughness prediction in surface grinding. In The IASTED international conference on artificial intelligence and applications (AIA 2007) (pp. 96–101).

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