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An intelligent systems approach for detecting defects in aircraft composites by using air-coupled ultrasonic testing.

机译：一种通过使用空气耦合超声测试来检测飞机复合材料缺陷的智能系统方法。

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摘要

Circular air-coupled ultrasonic testing (ACUT) setup for the inspection of commercial carbon-carbon composite aircraft brake disks was developed in Intelligent Measurement and Evaluation Laboratory (IMEL) at Southern Illinois University Carbondale (SIUC). The developed test setup utilizes Airstar single channel air-coupled equipment and has only manual A-scan and B-scan capability. The developed ACUT technique is unique compared to the commercial C-scan ultrasonic systems and is proficient, fast, economically feasible, and easy to implement method particularly for the inspection of carbon-carbon (C/C) composites aircraft brake disks.;Prior to conducting air-coupled measurements, wobble analysis was carried out. This was important because significant wobbling in the test setup can lead to the interference of the reflected and the incident beam which would result to inaccurate ultrasonic measurements. The measured deviation due to wobbling, surface profile of the disk, design, and experimental error were relatively small. Therefore, these errors were neglected while performing ACUT measurements.;For ACUT measurements, several through-transmitted amplitude signals were recorded within the C/C brake disks manually. The images were then reconstructed using Matlab based on the through-transmitted amplitude signals. Finally, a comparison was drawn between the reconstructed images and the C-scan images of the C/C brake disks obtained from the commercial Airstar C-scan ACUT system. Like commercial C-scan ACUT image results, reconstructed images were also able to detect all defects in the commercial C/C brake disks which served for the system verification and validation.;In addition, defect, non-defect, and suspected areas within the C/C brake disks were quantified with air-coupled measurement. For this, light microscopy was conducted for every sample made from each C/C brake disks at lower magnification of 10X. It was concluded that it is very difficult to assess the crack or delamination situation based on a 2D micrograph of one depth. Also, it was concluded that an internal porosity and micro-cracks may not be only factors that can be related to defects.;Finally, an intelligent systems approach, specifically, fuzzy logic and artificial neural network (ANN) methodologies were implemented for the automatic defect detection in commercial C/C aircraft brake disks by using air-coupled ultrasonic results. For this, a multi-layer perceptron (MLP) with two hidden layers and a scaled conjugate gradient back-propagation (BP) learning algorithm was used for the ANN training. The network training process was performed in an off-line mode using the ANN toolbox in Matlab. The network training was repeated until a steady state was reached, where there was no further change in the synaptic weights. The ANN provided plausible results in detecting the defect areas for different C/C brake disks. It was also demonstrated that the system was able to learn the rules without knowing any algorithm for automatic defect detection.
机译：美国南伊利诺伊大学卡本代尔分校（SIUC）的智能测量和评估实验室（IMEL）开发了用于检查商用碳-碳复合材料飞机制动盘的圆形空气耦合超声测试（ACUT）装置。开发的测试设置使用Airstar单通道空气耦合设备，并且仅具有手动A扫描和B扫描功能。相比于商用C扫描超声系统，已开发的ACUT技术独树一帜，并且是一种熟练，快速，经济可行且易于实施的方法，特别是用于检查碳-碳（C / C）复合材料飞机刹车盘的方法。进行空气耦合测量时，进行了摆动分析。这一点很重要，因为测试设置中的明显摆动会导致反射光束和入射光束的干涉，从而导致超声测量不准确。由于摆动，磁盘的表面轮廓，设计和实验误差而导致的测量偏差相对较小。因此，在执行ACUT测量时可以忽略这些误差。对于ACUT测量，在C / C制动盘中手动记录了几个通过的幅度信号。然后，使用Matlab根据透射的幅度信号重建图像。最后，在从商用Airstar C扫描ACUT系统获得的C / C制动盘的重建图像和C扫描图像之间进行比较。像商用C扫描ACUT图像结果一样，重建的图像也能够检测到商用C / C制动盘中的所有缺陷，这些缺陷可用于系统验证和确认。此外，在缺陷，无缺陷和可疑区域内C / C制动盘通过空气耦合测量进行定量。为此，对每个由C / C制动盘制成的样品进行光学显微镜检查，放大倍数为10X。结论是，基于一个深度的2D显微照片很难评估裂纹或分层情况。此外，得出的结论是内部孔隙率和微裂纹可能不仅是与缺陷有关的因素。最后，针对自动系统实现了智能系统方法，特别是模糊逻辑和人工神经网络（ANN）方法。利用空气耦合超声结果检测商用C / C飞机制动盘中的缺陷。为此，将具有两个隐藏层的多层感知器（MLP）和按比例缩放的共轭梯度反向传播（BP）学习算法用于ANN训练。使用Matlab中的ANN工具箱以离线模式执行网络培训过程。重复网络训练直到达到稳定状态，此时突触权重没有进一步变化。 ANN为检测不同C / C制动盘的缺陷区域提供了合理的结果。还证明了该系统能够学习规则而无需知道任何自动缺陷检测算法。

著录项

  • 作者

    Poudel, Anish.;

  • 作者单位

    Southern Illinois University at Carbondale.;

  • 授予单位 Southern Illinois University at Carbondale.;
  • 学科 Engineering Mechanical.;Artificial Intelligence.
  • 学位 M.S.
  • 年度 2011
  • 页码 160 p.
  • 总页数 160
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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