基于混沌表示和特征注意力机制的机床两轴动态误差预测

针对传统方法难以揭示机床多轴插补动态误差的序列产生机制,各时间维度上的误差时序特征存在相互关联的问题,提出一种融合混沌表示(Chaotic representation, CR)和特征注意力机制(Feature attention mechanism, FA)的级联动态误差预测模型。首先,在证明多元动态误差时变演化具有混沌特性的基础上,对其进行相空间重构,将动态误差参数时间序列背后隐藏的信息在相空间中进行表达。然后,融合特征注意力机制在时间维度上动态分配相点特征权重的同时降低高维演化相空间信息冗余,进一步重塑原系统的动力学状态向量空间。最后,考虑到混沌时变演化具有长程相关性,采用双向长短期记忆(Bi-directional long short-term memory, Bi-LSTM)网络模型逼近混沌相空间内的动力学特性,实现动态误差混沌时间序列信息的有效预测。通过XK-L540型数控铣床实测数据的算例表明,相较于CRFA-LSTM模型,以及单一级联模型CR-Bi-LSTM、FA-Bi-LSTM,本文算法的均方根误差分别降低约35%、16%和43%。

Dynamic Error Prediction of Machine Tool Two-axis Based on Chaotic Representation and Feature Attention Mechanism

To address the problem that traditional methods are difficult to reveal the sequence generation mechanism of dynamic error in machine tool multi-axis interpolation and the error time series features in each time dimension are interrelated, a cascaded dynamic error prediction model integrating chaotic representation (CR) and feature attention mechanism (FA) was proposed. Firstly, on the basis of proving that the time-varying evolution of multivariate dynamic error had chaotic characteristics, the phase space was reconstructed to represent the hidden information behind the time series of dynamic error parameters in the phase space. Then the fused feature attention mechanism further reshaped the dynamical state vector space of the original system by dynamically assigning phase point feature weights in the time dimension while reducing the redundancy of information in the high-dimensional evolution phase space. Finally, considering the long-range correlation of chaotic time-varying evolution, the bi-directional long short-term memory (Bi-LSTM) network model was used to approximate the dynamics in the chaotic phase space to achieve the effective prediction of dynamic error chaotic time series information. Compared with the Bi-LSTM model and the single cascade models CR-Bi-LSTM and FA-Bi-LSTM, the root mean square error of this algorithm was reduced by about 35%, 16% and 43%, respectively, as shown by the example of XK-L540 CNC milling machine with real data. The algorithm realized the phase space expression of dynamic error sequence generation mechanism in time dimension, and constantly played the main role of key phase point feature, with high prediction accuracy.