LOW-SPEED MOTION CONTROL EXPERIMENTS ON A SINGLE-POINT DIAMOND TURNING MACHINE USING CMAC LEARNING CONTROL ALGORITHM

Diamond turning of brittle materials such as glass, ceramic, germanium , and zinc sulfide has been of considerable research interest in recen t years due to applications in optics and precision engineering system s. 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 lo w depth of cut and feed rates. Furthermore, the tool positioning accur acy of the machine must be in the nanometer range to obtain optical qu ality machined parts with surface finish and profile accuracy on the o rder 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 b acklash, Friction at extremely low feed rates is highly nonlinear due to the transition fi om stiction to Coulomb friction, and as such is v ery difficult to model. Standard proportional-integral-derivative (PID ) type controllers are unable to deal with this large and erratic fric tion within the requirements of ultra precision machining. In order to compensate the effects of friction in the machine tool axes, a learni ng controller based on the Cerebellar Model Articulation Controller (C MAC) neural network is studied for servo-control. The learning control ler was implemented using ''C'' language on a DSP based controller for a single point diamond turning machine. The CMAC servo control algori thm improved the positioning accuracy of the single point diamond turn ing machine by a factor of 10 compared to the standard PID algorithm r un on the same machine and control system hardware.