Optimization of Computer Numerical Control Interpolation Parameters Using a Backpropagation Neural Network and Genetic Algorithm with Consideration of Corner Vibrations

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...

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Bibliographic Details
Published in:Applied sciences 2021-02, Vol.11 (4), p.1665
Main Authors: Tseng, Hsiang-Chun, Tsai, Meng-Shiun, Cheng, Chih-Chun, Li, Chen-Jung
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Back propagation
Back propagation networks
contour error
Contours
Correlation coefficient
Correlation coefficients
data driven
Design optimization
Experiments
Genetic algorithms
Interpolation
interpolator
Libraries
machine performance
Machining
Model accuracy
Neural networks
Objective function
Operators (mathematics)
Optimization
Position errors
Velocity
Vibration
Vibration analysis
Vibrations
Weighting
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11041665