Adaptive optimization using grey relational analysis and PID control of CNC drilling process

Adaptive optimization and control of machining process is of great significance due to cost saving and better tool utilization. This paper describes the adaptive optimization of drilling process using grey relational analysis. The PID control is used to implement the optimization strategy to automatically adjust the spindle speed and feed of the CNC servo drive system. For this purpose, the tool wear and the Metal Removal Rate are modeled using Neural networks.

The inputs to the model are the spindle speed, feed and vibration signals produced as a result of machining. The models show an deviation of 3% and 2% respectively. The control strategy is simulated using MATLAB simulink. The simulation results shows that the servo mechanism tracks the optimized values with an overall accuracy of 97%.

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