利用相控阵超声在焊缝检测中定位探头

Adam Gilmour;Alexander Ulrichsen;William Jackson;Morteza Tabatabaeipour;Gordon Dobie;Charles N. Macleod;Paul Murray;Benjamin Karkera
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摘要

本文开发了一种基于图像处理的工业资产焊缝远程无损检测定位系统。大型结构的人工超声检测通常是重复的,耗时的,并且从机器人支持中受益匪浅,然而,这些机器人系统通常固定为单一目的,缺乏对周围环境的自我意识,并且可能仅限于简单的几何形状。对于通常使用相控阵超声检测进行的焊缝检查,依赖于使用表面特征进行自动跟踪,例如焊接帽的激光轮廓。对于更复杂的几何形状的检查,例如非线性或鞍形焊缝,需要更位置敏感的方法。该系统利用了无损检测人员已经可以获得的实时相控阵超声图像信息,利用非表面的体积数据来估计焊缝的位置。使用安装在小型机器人机械手末端执行器上的64元件,10 mhz相控阵探头捕获数据,与市场上的替代品相比,由于其灵活性更高,因此增加了应用范围。对超声数据进行形态学处理,降低母材区域的噪声,增强焊缝材料内部晶界反射的数据。通过一系列图像处理技术,可以预测被检焊缝的位置,绝对平均位置误差为$\ mathm {0.8 \text {m} \text {m}}$。通过这项研究,定位系统将嵌入到远程系统中,用于大型结构焊缝的广泛数据采集。
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Using Phased Array Ultrasound to Localize Probes During the Inspection of Welds
In this article, an image processing-based localization system is developed for remote nondestructive evaluation of welds within industrial assets. Manual ultrasonic inspection of large-scale structures is often repetitive, time-consuming, and benefits greatly from robotic support, however, these robotic systems are often fixed to a single purpose, lack self-awareness of their surrounding environment, and can be limited to simple geometry. For the inspection of welds, which are often carried out using phased array ultrasonic testing, there is a reliance on the use of surface features for automated tracking such as the laser profiling of a weld cap. For the inspection of more complex geometry such as nonlinear or saddle welds, a more positionally sensitive method is required. The proposed system utilizes information already available to a nondestructive inspector in the form of live phased array ultrasonic images to estimate the location of the weld using nonsurface, volumetric data. Data is captured using a 64-element, 10-MHz phased array probe mounted to the end effector of a small robotic manipulator which increases the scope of applications due to its heightened flexibility when compared to on-the-market alternatives. Morphological operations are applied to the ultrasonic data to reduce the noise apparent from regions of parent material and promote the data reflected from grain boundaries within the weld material. Through a series of image processing techniques, it is possible to predict the position of a weld under inspection with an absolute mean positional error of $\mathrm {0.8 \text {m} \text { m} }$ . From this study, the localization system is to be embedded within a remote system for extensive data acquisition of welds on large structures.
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