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使用工业机器人生成复杂几何形状零件高质量超声图像的方法。

Methodology for the Generation of High-Quality Ultrasonic Images of Complex Geometry Pieces Using Industrial Robots.

机构信息

Institute for Physical and Information Technologies "Leonardo Torres Quevedo", ITEFI, Spanish National Research Council (CSIC), 28006 Madrid, Spain.

Tecnitest Ingenieros SL, 28021 Madrid, Spain.

出版信息

Sensors (Basel). 2023 Mar 1;23(5):2684. doi: 10.3390/s23052684.

DOI:10.3390/s23052684
PMID:36904889
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10007159/
Abstract

Industrial robotic arms integrated with server computers, sensors and actuators have revolutionized the way automated non-destructive testing is performed in the aeronautical sector. Currently, there are commercial, industrial robots that have the precision, speed and repetitiveness in their movements that make them suitable for use in numerous non-destructive testing inspections. Automatic ultrasonic inspection of complex geometry parts remains one of the most difficult challenges in the market. The closed configuration, i.e., restricted access to internal motion parameters, of these robotic arms makes it difficult for an adequate synchronism between the movement of the robot and the acquisition of the data. This is a serious problem in the inspection of aerospace components, where high-quality images are necessary to assess the condition of the inspected component. In this paper, we applied a methodology recently patented for the generation of high-quality ultrasonic images of complex geometry pieces using industrial robots. The methodology is based on the calculation of a synchronism map after a calibration experiment and to introduce this corrected map in an autonomous, independent external system developed by the authors to obtain precise ultrasonic images. Therefore, it has been shown that it is possible to establish the synchronization of any industrial robot with any ultrasonic imaging generation system to generate high-quality ultrasonic images.

摘要

工业机械臂与服务器计算机、传感器和执行器集成在一起，彻底改变了航空航天领域自动化无损检测的方式。目前，市场上有一些商业和工业机器人，它们具有高精度、高速度和重复性的运动，非常适合用于多种无损检测检查。复杂几何形状零件的自动超声波检测仍然是市场上最具挑战性的难题之一。这些机械臂的封闭结构，即限制了对内部运动参数的访问，使得机器人的运动和数据采集之间很难达到足够的同步。这在航空航天部件的检测中是一个严重的问题，因为需要高质量的图像来评估被检测部件的状况。在本文中，我们应用了一种最近获得专利的方法，该方法使用工业机器人生成复杂几何形状零件的高质量超声波图像。该方法基于在校准实验后计算同步图，并将该校正图引入到由作者开发的自主、独立的外部系统中，以获得精确的超声波图像。因此，已经证明可以建立任何工业机器人与任何超声波成像生成系统的同步，以生成高质量的超声波图像。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/e634f0c2aa68/sensors-23-02684-g017.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/77074867c375/sensors-23-02684-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/810dcbbd72d7/sensors-23-02684-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/e1d072a3f048/sensors-23-02684-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/96d05199dd82/sensors-23-02684-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/e634f0c2aa68/sensors-23-02684-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/e282b0695c47/sensors-23-02684-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/f832ad8cd854/sensors-23-02684-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/0b2a9bc7baad/sensors-23-02684-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/d12a26662ffa/sensors-23-02684-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/8316fb8516c3/sensors-23-02684-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/341032a79863/sensors-23-02684-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/e8745da41cc9/sensors-23-02684-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/411b4d52fe07/sensors-23-02684-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/9b8b66910dbf/sensors-23-02684-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/e6dcb176eb1d/sensors-23-02684-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/dbb3e60dc589/sensors-23-02684-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/e03ae639bf67/sensors-23-02684-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/77074867c375/sensors-23-02684-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/810dcbbd72d7/sensors-23-02684-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/e1d072a3f048/sensors-23-02684-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/96d05199dd82/sensors-23-02684-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3548/10007159/e634f0c2aa68/sensors-23-02684-g017.jpg

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本文引用的文献

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