An image tracking system for welded seams using fuzzy logic
An image tracking system for welded seams using fuzzy logic
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DOI：
10.1016/s0924-0136(01)01155-4
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发表时间：
2002-01
影响因子：
6.3
通讯作者：
H. Kuo;Li-Jen Wu
中科院分区：
文献类型：
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作者：
H. Kuo;Li-Jen Wu
自动焊接是当今造船行业的一项重要技术，它可以提高焊接过程的质量。过去进行焊接需要大量的人力，但由于最近焊接方法和自动化技术的改进，目前的趋势是半自动焊接。然而，在焊接过程中，仍然需要一些人力来指导半自动焊接系统。由于焊接过程中涉及的高温，以及需要使用气体保护焊接区域，操作员无法非常密切地监视和控制过程。人为的操作误差和自然的焊接环境因素会导致焊缝坐标的误差，影响焊接质量。纠正这些问题耗费了大量的时间和人力。本文针对这一问题，将模糊理论与图像处理技术相结合，对焊缝进行分析，并向数控机床发出合适的控制信号。这将减少监督和纠正焊接过程所需的人力，并将使焊接过程朝着全自动焊接的目标迈进。建议的焊接监控流程如下。第一步是使用ccd设备获取实际焊接件和焊缝的图像。然后利用模糊理论和边缘算子检测方法生成图像的灰度特征因子和隶属函数。这些信息被输入决策逻辑，决策逻辑通过线性回归确定正确的焊缝坐标。在这项研究中，考虑了三种不同的焊缝：直线、扭结直线和曲线。这些坐标被传输到NC机床的控制代码以校正焊接过程。实验发现，光照环境的调整直接影响焊缝图像跟踪的过程。
Nowadays, automatic welding is an important technology in the shipbuilding industry since it can improve the quality of the welding process. In the past a large volume of manpower was necessary to carry out welding, but, due to recent improvements in welding methods and automation technology, the current trend is towards semi-automatic welding. However, some manpower is still required to guide the semi-automated welding system during welding. Due to the high temperatures involved in the welding process and the need to protect the welded area using gas, operators are not able to monitor and control the process very closely. Human operational error and natural welding environmental factors lead to errors in the weld seam coordinates, which influence the quality of the welding. Rectification of these problems consumes much time and manpower. The aim of this paper is to address this problem by using fuzzy theory coupled with image processing techniques to analyze the welded seam and to send an appropriate control signal to the numerical control (NC) machine. This will reduce the manpower required to supervise and correct the welding process, and will move the welding process towards the goal of fully automatic welding. The proposed welding monitoring and control process is as follows. The initial step is to use CCD equipment to obtain images of the actual weldment and welded seam. Fuzzy theory and edge-operator-detection methods are then used to generate the gray level feature factors and the membership function of the image. This information is input into decision-making logic, which, through linear regression, determines the correct welded seam coordinates. In this study, three different welds are considered: a straight line, a kinked straight line and a curved line. These coordinates are transferred to the control code of the NC machine to correct the welding process. It was found through experimentation that adjusting the lighting environment directly influences the process of image tracking of the welded seams.