Modeling for CNC Machine Tool Thermal Error Based on Genetic Algorithm Optimization Wavelet Neural Networks

被引：16
|
作者：
Li B. [1 ]
Zhang Y. [1 ]
Wang L. [1 ]
Li X. [1 ]
机构：
[1] Beijing Key Lab of Precision/Uitra-precision Manufacturing Equipment and Control, Tsinghua University, Beijing
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2019年 / 55卷 / 21期
关键词：
Error compensation; Genetic algorithm optimization; Machine tool; Thermal error; Wavelet neural networks;
D O I：
10.3901/JME.2019.21.215
中图分类号：
学科分类号：
摘要：
Thermal error has been a significant factor influencing the accuracy of CNC machine tools,in order to maximize the accuracy and efficiency of thermal error compensation for CNC machine tools, a novel thermal error compensation model based on genetic algorithm for optimizing wavelet neural network is presented by combining the advantages of adaptive global optimization searching ability of genetic algorithm and good time-frequency local characteristics of wavelet neural network. Taking a five-axis swing horizontal machining center as the test object, the thermal error prediction model of wavelet neural network model is established with the temperature variables and thermal errors of machine tools as input samples. Then the weights and thresholds of wavelet neural network are adjusted by genetic algorithm, and the thermal error prediction model is finally established. Compared with traditional artificial neural network and ordinary wavelet neural network, the new compensation model has the advantages of high precision, strong anti-disturbance ability and robustness. This model is expected to be used in thermal error prediction and comp-ensation of the complex industrial applications. © 2019 Journal of Mechanical Engineering.
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页码：215 / 220
页数：5
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