Abstract
A dynamic intelligent prediction control system is built in slender cylindrical grinding.
Elman network is used in the dynamic size prediction control model, and the first and the second
derivative of the actual amount removed from the workpiece are added into the network input,
which can greatly improve the size dynamic prediction accuracy. Moreover, a surface roughness
equation with vibration data is proposed. Based the equation, the surface roughness dynamic fuzzy
neural network prediction subsystem is built. Experiment verifies that the developed prediction
control system is feasible and has high prediction and control accuracy.
Publisher
Trans Tech Publications, Ltd.
Subject
Mechanical Engineering,Mechanics of Materials,General Materials Science
Reference8 articles.
1. G. Chryssolouris, M. Guillot and M. Domroese: Journal of Engineering for Industry, ASME. Vol. 110 (1988), pp.397-398.
2. E. Brinkmeier, H.K. Tšnshoff, C. Czenkusch and C. Heinzel: Journal of Intelligent Manufacturing, Vol. 9 (1998), pp.303-314.
3. C.W. Lee and Y.C. Shin: Proc. the Third International Conference on Intelligent Processing and Manufacturing of Materials (Vancouver, Canada, Jul. 29th-Aug. 2nd, 2001). pp.829-838.
4. N. Ding, L.S. Wang and G.F. Li: Key Engineering Materials, Vols. 259-260 (2004), pp.333-337.
5. S. Malkin: Theory and Application of Grinding Technology (2002), p.10.