Development of a CNN-based real-time monitoring algorithm for additively manufactured molybdenum
Development of a CNN-based real-time monitoring algorithm for additively manufactured molybdenum
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开发基于CNN的增材制造钼实时监测算法
DOI：
10.1016/j.sna.2023.114205
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Shin, Seung-Jun
中科院分区：
文献类型：
--
作者：
Kim, Eun-Su;Lee, Dong-Hee;Seo, Gi-Jeong;Kim, Duck-Bong;Shin, Seung-Jun
提出了一种基于卷积神经网络(CNN)的实时监测算法，用于检测钼丝+弧添加剂制造(WAAM)过程中的异常情况。该算法由三个模块组成：图像转换、CNN预测和实时监控。图像转换模块将时间序列电压波形数据的形式转换为电压图像数据。CNN预测模块将每个电压图像分类为正常图像或异常图像。实时监控模块将CNN预测模型的结果表示在实时仪表板上。通过对钼材料单珠的实验，验证了该算法的有效性。结果表明，该方法能够实时、高精度地检测出异常的WAAM过程。此外，还对电压图像数据的不同间隔和带宽进行了灵敏度分析，这是该方法的主要输入参数。在此调查的基础上，建立了设置间隔和带宽的指导原则。最后，应用类激活映射方法验证了CNN分类器的有效性。结论是，CNN分类器经过了充分的训练，因为它们捕捉到了正常和异常情况下电压图像中的关键区域。
A convolutional neural network (CNN)-based real-time monitoring algorithm is present to detect an abnormal wire + arc additive manufacturing (WAAM) process for molybdenum. The proposed algorithm consists of three modules: image conversion, CNN prediction, and real-time monitoring. The image conversion module changes the form of a time-series voltage waveform data into voltage image data. The CNN prediction module classifies each voltage image into a normal or abnormal image. The real-time monitoring module expresses the results of the CNN prediction model on a real-time dashboard. Experiments for single beads of molybdenum materials were performed to validate the performance of the proposed algorithm. It was observed that abnormal WAAM processes are detected in real-time with high accuracy. In addition, a sensitivity analysis with respect to different intervals and bandwidths of the voltage image data was conducted, which are the main input parameters of the proposed method. Based on this investigation, guidelines for setting the interval and bandwidth were established. Finally, the effectiveness of the CNN classifiers was validated by applying a class-activation mapping method. It was concluded that the CNN classifiers were adequately trained because they captured the critical regions in the voltage images for both normal and abnormal cases.