Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Institute of Intelligent Manufacturing Technology, Shenzhen Polytechnic, Shenzhen 518055, China.
Sensors (Basel). 2023 May 9;23(10):4595. doi: 10.3390/s23104595.
Tool wear condition monitoring is an important component of mechanical processing automation, and accurately identifying the wear status of tools can improve processing quality and production efficiency. This paper studied a new deep learning model, to identify the wear status of tools. The force signal was transformed into a two-dimensional image using continuous wavelet transform (CWT), short-time Fourier transform (STFT), and Gramian angular summation field (GASF) methods. The generated images were then fed into the proposed convolutional neural network (CNN) model for further analysis. The calculation results show that the accuracy of tool wear state recognition proposed in this paper was above 90%, which was higher than the accuracy of AlexNet, ResNet, and other models. The accuracy of the images generated using the CWT method and identified with the CNN model was the highest, which is attributed to the fact that the CWT method can extract local features of an image and is less affected by noise. Comparing the precision and recall values of the model, it was verified that the image obtained by the CWT method had the highest accuracy in identifying tool wear state. These results demonstrate the potential advantages of using a force signal transformed into a two-dimensional image for tool wear state recognition and of applying CNN models in this area. They also indicate the wide application prospects of this method in industrial production.
刀具磨损状态监测是机械加工自动化的重要组成部分，准确识别刀具的磨损状态可以提高加工质量和生产效率。本文研究了一种新的深度学习模型，用于识别刀具的磨损状态。利用连续小波变换（CWT）、短时傅里叶变换（STFT）和Gramian 角总和场（GASF）方法将力信号转换为二维图像。然后，将生成的图像输入到所提出的卷积神经网络（CNN）模型中进行进一步分析。计算结果表明，本文提出的刀具磨损状态识别的准确率在 90%以上，高于 AlexNet、ResNet 等模型的准确率。使用 CWT 方法生成的图像并使用 CNN 模型识别的准确率最高，这归因于 CWT 方法可以提取图像的局部特征，并且受噪声的影响较小。通过比较模型的精度和召回值，验证了 CWT 方法获得的图像在识别刀具磨损状态方面具有最高的准确性。这些结果表明，使用力信号转换为二维图像进行刀具磨损状态识别以及在该领域应用 CNN 模型具有潜在的优势。它们还表明，这种方法在工业生产中有广泛的应用前景。
