School of Modern Post, Beijing University of Posts and Telecommunications, Beijing 100876, China.
School of Artificial and Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2023 Jan 21;23(3):1240. doi: 10.3390/s23031240.
Tool wear is a key factor in the machining process, which affects the tool life and quality of the machined work piece. Therefore, it is crucial to monitor and diagnose the tool condition. An improved CaAt-ResNet-1d model for multi-sensor tool wear diagnosis was proposed. The ResNet18 structure based on a one-dimensional convolutional neural network is adopted to make the basic model architecture. The one-dimensional convolutional neural network is more suitable for feature extraction of time series data. Add the channel attention mechanism of CaAt1 to the residual network block and the channel attention mechanism of CaAt5 automatically learns the features of different channels. The proposed method is validated on the PHM2010 dataset. Validation results show that CaAt-ResNet-1d can reach 89.27% accuracy, improving by about 7% compared to Gated-Transformer and 3% compared to Resnet18. The experimental results demonstrate the capacity and effectiveness of the proposed method for tool wear monitor.
刀具磨损是加工过程中的一个关键因素，它会影响刀具寿命和加工工件的质量。因此，监测和诊断刀具状况至关重要。提出了一种改进的 CaAt-ResNet-1d 模型，用于多传感器刀具磨损诊断。该模型采用基于一维卷积神经网络的 ResNet18 结构作为基本模型架构。一维卷积神经网络更适合于时间序列数据的特征提取。在残差网络块中添加 CaAt1 的通道注意力机制和 CaAt5 的通道注意力机制，自动学习不同通道的特征。该方法在 PHM2010 数据集上进行了验证。验证结果表明，CaAt-ResNet-1d 的准确率可达 89.27%，与 Gated-Transformer 相比提高了约 7%，与 Resnet18 相比提高了 3%。实验结果证明了该方法在刀具磨损监测方面的能力和有效性。
