Song Lin, Tan Jianying
School of Intelligent Manufacturing, Panzhihua University, Panzhihua 617000, China.
Sensors (Basel). 2024 Jun 29;24(13):4244. doi: 10.3390/s24134244.
The spindle rotation error of computer numerical control (CNC) equipment directly reflects the machining quality of the workpiece and is a key indicator reflecting the performance and reliability of CNC equipment. Existing rotation error prediction methods do not consider the importance of different sensor data. This study developed an adaptive weighted deep residual network (ResNet) for predicting spindle rotation errors, thereby establishing accurate mapping between easily obtainable vibration information and difficult-to-obtain rotation errors. Firstly, multi-sensor data are collected by a vibration sensor, and Short-time Fourier Transform (STFT) is adopted to extract the feature information in the original data. Then, an adaptive feature recalibration unit with residual connection is constructed based on the attention weighting operation. By stacking multiple residual blocks and attention weighting units, the data of different channels are adaptively weighted to highlight important information and suppress redundancy information. The weight visualization results indicate that the adaptive weighted ResNet (AWResNet) can learn a set of weights for channel recalibration. The comparison results indicate that AWResNet has higher prediction accuracy than other deep learning models and can be used for spindle rotation error prediction.
计算机数控（CNC）设备的主轴旋转误差直接反映了工件的加工质量，是反映CNC设备性能和可靠性的关键指标。现有的旋转误差预测方法没有考虑不同传感器数据的重要性。本研究开发了一种自适应加权深度残差网络（ResNet）用于预测主轴旋转误差，从而在易于获取的振动信息和难以获取的旋转误差之间建立准确的映射关系。首先，通过振动传感器采集多传感器数据，并采用短时傅里叶变换（STFT）提取原始数据中的特征信息。然后，基于注意力加权操作构建具有残差连接的自适应特征重新校准单元。通过堆叠多个残差块和注意力加权单元，对不同通道的数据进行自适应加权，以突出重要信息并抑制冗余信息。权重可视化结果表明，自适应加权ResNet（AWResNet）可以学习到一组用于通道重新校准的权重。比较结果表明，AWResNet比其他深度学习模型具有更高的预测精度，可用于主轴旋转误差预测。
