Cruz Yarens J, Rivas Marcelino, Quiza Ramón, Beruvides Gerardo, Haber Rodolfo E
Centro de Estudio de Fabricación Avanzada y Sostenible, Universidad de Matanzas, Matanzas 40100, Cuba.
Social Innovation Business, Hitachi Europe Ltd., 40547 Hitachi, Germany.
Sensors (Basel). 2020 Aug 12;20(16):4505. doi: 10.3390/s20164505.
One of the most important operations during the manufacturing process of a pressure vessel is welding. The result of this operation has a great impact on the vessel integrity; thus, welding inspection procedures must detect defects that could lead to an accident. This paper introduces a computer vision system based on structured light for welding inspection of liquefied petroleum gas (LPG) pressure vessels by using combined digital image processing and deep learning techniques. The inspection procedure applied prior to the welding operation was based on a convolutional neural network (CNN), and it correctly detected the misalignment of the parts to be welded in 97.7% of the cases during the method testing. The post-welding inspection procedure was based on a laser triangulation method, and it estimated the weld bead height and width, with average relative errors of 2.7% and 3.4%, respectively, during the method testing. This post-welding inspection procedure allows us to detect geometrical nonconformities that compromise the weld bead integrity. By using this system, the quality index of the process was improved from 95.0% to 99.5% during practical validation in an industrial environment, demonstrating its robustness.
在压力容器制造过程中，最重要的操作之一是焊接。该操作的结果对容器完整性有很大影响；因此，焊接检测程序必须检测出可能导致事故的缺陷。本文介绍了一种基于结构光的计算机视觉系统，通过结合数字图像处理和深度学习技术，用于液化石油气（LPG）压力容器的焊接检测。焊接操作前应用的检测程序基于卷积神经网络（CNN），在方法测试期间，它在97.7%的情况下正确检测出待焊接部件的不对中。焊接后检测程序基于激光三角测量法，在方法测试期间，它估计焊缝高度和宽度的平均相对误差分别为2.7%和3.4%。这种焊接后检测程序使我们能够检测出损害焊缝完整性的几何不符合项。通过使用该系统，在工业环境中的实际验证期间，该工艺的质量指标从95.0%提高到了99.5%，证明了其鲁棒性。
