基于卷积神经网络的电阻点焊失效模式分类

Watchanun Piriyabunjerd, Chettapong Janya-anurak
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引用次数: 0

摘要

电阻点焊(RSW)是一种广泛应用于汽车工业的焊接工艺。然而,在生产线上,人们通常从焊缝的外观来粗略地评估焊缝的质量。在这项工作中,我们提出了使用卷积神经网络(CNN)来预测电阻点焊的质量。在这项工作中应用的CNN架构是MobileNetV3。在本工作中,焊缝的质量是由拉伸剪切试验中焊缝的强度决定的RSW的破坏模式。利用焊缝的外部视像作为预测焊缝质量的信息。为了建立数据集,在特定的焊接条件下进行RSW,并捕获焊缝的热迹图像。CNN的参数由焊缝的视像及其失效模式进行训练。结果表明,CNN模型能够从未见验证数据集的焊缝图像中预测出RSW的失效模式类别,f1得分为94.32%,令人满意。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of the Resistance Spot Weld Failure Mode Using Convolutional Neural Network
The resistance spot welding (RSW) is a comprehensive used process in the automotive industry. However, in the production line the quality of the weld is normally roughly assessed from its appearance by human. In this work, we propose the prediction of the quality of the resistance spot weld by using the convolutional neural network (CNN). The architecture of CNN applied in this work was MobileNetV3. The quality of the weld in this work was the failure mode of RSW determined by strength of the weld by tensile shear test. The external apparent image of welds was used as information for predicting the quality of the welds. For building the data set, the RSW was conducted with specific welding conditions and the heat trace image of welds was captured. The parameters of the CNN were trained from the apparent image of the welds and their failure mode. As a result, the CNN model was able to predict the class of the failure mode of the RSW from the welds image with satisfactory F1-score of 94.32% for unseen validation data set.
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