Development of Image Sensing Technology for Automatic Welding (Image Recognition by Deep Learning)

Tsuyoshi ASHIDA; Akira OKAMOTO; Keita OZAKI; Masatoshi HIDA; Takayoshi YAMASHITA

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Development of Image Sensing Technology for Automatic Welding (Image Recognition by Deep Learning)

机译：自动焊接图像传感技术的开发（深度学习的图像识别）

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A system has been developed for automatic MAG welding with ceramic backing. This system comprises a camera to capture the images of the molten pool for recognizing feature points to control the torch. A regression-based deep convolutional neural network (DCNN), which outputs continuous values from image inputs, was used to recognize feature points such as arc center and the leading end of the molten pool. This has enabled the accurate recognition of the distance from the arc center to the leading end of the molten pool, as well as the width of the molten pool, with an average error of 0.44 mm or less. The formation of a proper back bead has been confirmed in a welding experiment on a test piece with a tapered gap (from 3 to 10 mm).

机译：已经开发了一种用于自动MAG焊接带陶瓷背衬的系统。该系统包括相机，用于捕获熔池的图像以识别特征点以控制焊炬。从图像输入输出连续值的基于回归的深卷积神经网络（DCNN）来识别诸如弧形中心和熔池的前端的特征点。这使得能够精确地识别距离弧形中心到熔池的前端的距离，以及熔池的宽度，平均误差为0.44mm或更小。已经在具有锥形间隙（3至10mm）的试验片上的焊接实验中确认了适当的背珠的形成。

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来源
《Kobelco technology review》 |2019年第37期|77-81|共5页
作者
Tsuyoshi ASHIDA; Akira OKAMOTO; Keita OZAKI; Masatoshi HIDA; Takayoshi YAMASHITA;
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作者单位

AI Promotion Project Department Technical Development Group;

Production Systems Research Laboratory Technical Development Group;

Production Systems Research Laboratory Technical Development Group;

Production Systems Research Laboratory Technical Development Group;

Department of Computer Science College of Engineering Chubu University;

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Development of Image Sensing Technology for Automatic Welding (Image Recognition by Deep Learning)

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