Department of Mechanical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan.
Sensors (Basel). 2021 Jan 2;21(1):262. doi: 10.3390/s21010262.
Stamping is one of the most widely used processes in the sheet metalworking industry. Because of the increasing demand for a faster process, ensuring that the stamping process is conducted without compromising quality is crucial. The tool used in the stamping process is crucial to the efficiency of the process; therefore, effective monitoring of the tool health condition is essential for detecting stamping defects. In this study, vibration measurement was used to monitor the stamping process and tool health. A system was developed for capturing signals in the stamping process, and each stamping cycle was selected through template matching. A one-dimensional (1D) convolutional neural network (CNN) was developed to classify the tool wear condition. The results revealed that the 1D CNN architecture a yielded a high accuracy (>99%) and fast adaptability among different models.
冲压是金属板材加工行业中应用最广泛的工艺之一。由于对更快工艺的需求不断增加，确保冲压过程在不影响质量的情况下进行至关重要。冲压过程中使用的工具对工艺效率至关重要；因此，有效监测工具的健康状况对于检测冲压缺陷至关重要。在本研究中，振动测量被用于监测冲压过程和工具健康状况。开发了一个系统来捕获冲压过程中的信号，并通过模板匹配选择每个冲压周期。开发了一维卷积神经网络（CNN）来对工具磨损状态进行分类。结果表明，一维 CNN 架构在不同模型之间具有很高的准确性（>99%）和快速适应性。
