An Intelligent Supervision System for Workpiece Size in Cylindrical Grinding

The paper presents an intelligent supervision system to optimize and control the work piece size during cylindrical grinding. The initial cylindrical grinding parameters are decided by the expert system based on neural network, Multi-feed and setting overshoot optimization methods are adopted in adaptation control subsystem, a variable parameter optimization adaptive control strategy is proposed in the deformation optimal control subsystem, Elman network is used in the size dynamic prediction subsystem, and the flexible factor is introduced to the fuzzy control subsystem, a human machine cooperation subsystem can revise the process parameters in real-time. The experiment of the cylindrical grinding was implemented. The results showed that this control system was valid, and could greatly improve the cylindrical grinding quality and machining efficiency.