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This paper presents an optimization algorithm for tuning the interpolation parameters of computer numerical control (CNC) controllers; it operates by considering multiple objective functions, namely, contour errors, the machining time (MT), and vibrations. The position commands, position errors, and vibration signals from 1024 experiments were considered in the designed trajectory. The experimental data—the maximum contour error (MCoE), MT, and corner vibration (CVib)—were analyzed to compute the performance index. A backpropagation neural network (BPNN) with 20 hidden layers was applied to predict the performance index. The correlation coefficients for the predicted values and experimental results for the MCoE, MT, and CVib based on the validation data were 0.9984, 0.9998, and 0.9354, respectively. The high correlation coefficients highlight the accuracy of the model for designing the interpolation parameter. After the BPNN model was developed, a genetic algorithm (GA) was adopted to determine the optimized parameters of the interpolation under different weighting of the performance index. A weighted sum approach involving the objective function was employed to determine the optimized interpolation parameters in the GA. Thus, operators can judge the feasibility of the interpolation parameter for various weighting settings. Finally, a mixed path was selected to verify the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an integrated servo-feed-drive model including the cutting force and structural effect to predict tracking errors in an end-milling process. Most conventional approaches consider the cutting force to be an equivalent torque to servo feed drive. However, in addition to acting as the equivalent torque to the servo feed drive, cutting forces also cause the machine table to vibrate. This paper considers the aforementioned cutting-force effects to predict tracking errors and then verifies the tracking errors using experimental results. Experiments are conducted on a 3-axis computer numerical control (CNC) machining center to validate the tracking errors predicted by the proposed servo-feed-drive model. For one case study, the peak-to-peak tracking errors from the experimental, proposed, and traditional models are 3 μm, 2.8 μm, and 0.5 μm, respectively, for the x-axis, and 2.1 μm, 1.7 μm, and 0.4 μm, respectively, for the y-axis. The experimental results illustrate that the tracking errors predicted using the proposed model are more accurate than those predicted using the traditional model without consideration of the transmission path. Therefore, it can be concluded that the proposed integrated model provides much accurate tracking-error prediction, and thus, the ball-screw and machine-table flexibilities should be considered. [ABSTRACT FROM AUTHOR]