Diamond turning of brittle materials such as glass, ceramic, germanium, and zinc sulfide has been of considerable research interest in recent years due to applications in optics and precision engineering systems. When diamond turning brittle materials, material removal should be kept within the ductile regime to avoid subsurface damage (Evans, 1991; Nakasuji et al., 1990). It is generally accepted that ductile regime machining of brittle materials can be accomplished using extremely low depth of cut and feed rates. Furthermore, the tool positioning accuracy of the machine must be in the nanometer range to obtain optical quality machined parts with surface finish and profile accuracy on the order of 10 nm and 100 nm respectively (Nakasuji et al, 1990, Ueda et al., 1991). Nanometric level positioning accuracy of the machine tool axes is difficult particularly at low feed rates due to friction and backlash. Friction at extremely low feed rates is highly nonlinear due to the transition from stiction to Coulomb friction, and as such is very difficult to model. Standard proportional-integral-derivative (PID) type controllers are unable to deal with this large and erratic friction within the requirements of ultra precision machining. In order to compensate the effects of friction in the machine tool axes, a learning controller based on the Cerebellar Model Articulation Controller (CMAC) neural network is studied for servo-control. The learning controller was implemented using “C” language on a DSP based controller for a single point diamond turning machine. The CMAC servo control algorithm improved the positioning accuracy of the single point diamond turning machine by a factor of 10 compared to the standard PID algorithm run on the same machine and control system hardware.
Low Speed Motion Control Experiments on a Single Point Diamond Turning Machine Using CMAC Learning Control Algorithm
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Larsen, G., and Cetinkunt, S. (December 1, 1997). "Low Speed Motion Control Experiments on a Single Point Diamond Turning Machine Using CMAC Learning Control Algorithm." ASME. J. Dyn. Sys., Meas., Control. December 1997; 119(4): 775–781. https://doi.org/10.1115/1.2802390
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