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Dynamic Error Compensation for Ball Screw Feed Drive Systems Based on Prediction Model

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Publicado en:Machines vol. 13, no. 5 (2025), p. 433
Autor Principal: Liu, Hongda
Outros autores: Guo Yonghao, Liu, Jiaming, Niu Wentie
Publicado:
MDPI AG
Materias:
Friction
Simulation
Accuracy
Neural networks
Prediction models
High speed machining
Motion control
Engineering
Hypercubes
Methods
Dynamic models
Tracking errors
Feed systems
Error compensation
Transfer functions
Efficiency
Latin hypercube sampling
Ball screws
Coupling
Acceso en liña:Citation/Abstract
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Resumo:The dynamic error is the dominant factor affecting multi-axis CNC machining accuracy. Predicting and compensating for dynamic errors is vital in high-speed machining. This paper proposes a novel prediction-model-based approach to predict and compensate for the ball screw feed system’s dynamic error. Based on the lumped and distributed mass methods, this method constructs a parameterized dynamic model relying on the moving component’s position for electromechanical coupling modeling. Using Latin Hypercube Sampling and numerical simulation, a sample set containing the input and output of one control cycle is obtained, which is used to train a Cascade-Forward Neural Network to predict dynamic errors. Finally, a feedforward compensation strategy based on the prediction model is proposed to improve tracking performance. The proposed method is applied to a ball screw feed system. Tracking error simulations and experiments are conducted and compared with the transfer function feedforward compensation. Typical trajectories are designed to validate the effectiveness of the electromechanical coupling model, the dynamic error prediction model, and the feedforward compensation strategy. The results show that the prediction model exhibits a maximum prediction deviation of 1.8% for the maximum tracking error and 13% for the average tracking error. The proposed compensation method with friction compensation achieves a maximum reduction rate of 76.7% for the maximum tracking error and 63.7% for the average tracking error.
ISSN:2075-1702
DOI:10.3390/machines13050433
Fonte:Engineering Database