Si Sumei, Mu Deqiang, Si Zekai
College of Electromechanical Engineering, Changchun University of Technology, Changchun, 130012, China.
Changchun University of Technology, Changchun, 130012, China.
Sci Rep. 2024 Sep 5;14(1):20754. doi: 10.1038/s41598-024-71795-4.
To ensure the reliability of machining quality, it is crucial to predict tool wear accurately. In this paper, a novel deep learning-based model is proposed, which synthesizes the advantages of power spectral density (PSD), convolutional neural networks (CNN), and vision transformer model (ViT), namely PSD-CVT. PSD maps can provide a comprehensive understanding of the spectral characteristics of the signals. It makes the spectral characteristics more obvious and makes it easy to analyze and compare different signals. CNN focuses on local feature extraction, which can capture local information such as the texture, edge, and shape of the image, while the attention mechanism in ViT can effectively capture the global structure and long-range dependencies present in the image. Two fully connected layers with a ReLU function are used to obtain the predicted tool wear values. The experimental results on the PHM 2010 dataset demonstrate that the proposed model has higher prediction accuracy than the CNN model or ViT model alone, as well as outperforms several existing methods in accurately predicting tool wear. The proposed prediction method can also be applied to predict tool wear in other machining fields.
为确保加工质量的可靠性，准确预测刀具磨损至关重要。本文提出了一种基于深度学习的新型模型，该模型综合了功率谱密度（PSD）、卷积神经网络（CNN）和视觉Transformer模型（ViT）的优点，即PSD-CVT。PSD图可以全面了解信号的频谱特征。它使频谱特征更加明显，便于分析和比较不同信号。CNN专注于局部特征提取，能够捕捉图像的纹理、边缘和形状等局部信息，而ViT中的注意力机制可以有效捕捉图像中存在的全局结构和长程依赖关系。使用两个带有ReLU函数的全连接层来获得预测的刀具磨损值。在PHM 2010数据集上的实验结果表明，所提出的模型比单独的CNN模型或ViT模型具有更高的预测精度，并且在准确预测刀具磨损方面优于几种现有方法。所提出的预测方法也可应用于预测其他加工领域的刀具磨损。
