Big data analysis of a mini three-axis CNC machine tool based on the tuning operation of controller parameters

The characteristic responses of a mini three-axis computer numerical control (CNC) machine tool based on the controller tuning operation were investigated for big data estimation. The major tuning parameters included the position control gains Kp, the position feed-forward control gains Kf, the speed control gains Kv, and the gain ratios Kg of the position and speed control values in manufacturing industries. Kp gains of 10, 30, 50, 80, 100, 200, 300, and 400 rad/s, Kf gains of 0, 30, 50, 60, 80, and 100 %, Kv gains of 30, 50, 70, 100, 300, 900, 2000, and 3000 rad/s, and Kg ratios of (1:1), (3:1), (5:1), and (7:1) were analyzed for smart productivity. The results show that the settling times at different Kp values were almost constant when the Kp gain was over 200 rad/s. The maximum overshoots, when the feed-forward gain is over 60 %, almost increased with increasing feed-forward gains. However, the overshoot of the three-axis CNC machine tool decreased as the Kv gain increased until the Kv gain reached 70 rad/s. The settling times at a constant Kg ratio decreased with an increase in the Kp and Kv gains. The characteristic responses of the tuning operations were enabled with connectivity to a cloud network to share the big data, to support decision making, and to adjust operations in real time.