Automated ultrasonic inspection of aerospace composites with enhanced defect detection probabilities aided by gantry deployed, CAD controlled robotics (AutoDISC)

下一代飞机将主要由碳复合材料组成。复合材料密集型飞机的失效模式尚不完全清楚，因为它们仍接近其设计寿命的开始，尽管很明显，它们易受表面不可见的内部冲击损伤的影响。然而，目前在生产和服务中的复合材料的NDI仍然主要是手工的，具有低面积覆盖率，并且由于宏观结构各向异性，NDI图像难以用任何技术解释。为了解决这些问题，该项目提出了两个关键的NDI创新：（1）使用部署的龙门架，CAD控制的机器人，高达100%的体积NDI覆盖率，以便在任何时候的检查记录都可以在连续的维护停机时间间隔内进行准确的比较，以进行健康诊断和预测。(2)通过推理引擎对图像中的空间和时间变化进行相似性分析，并通过贝叶斯分离进行相干噪声去除，实现复合缺陷的当前检测概率的阶跃增加。这些创新，使用超声波NDI验证，适用于任何类型的NDI传感器。

Future generation aircraft will consist mostly of carbon composite materials. The modes of failure in composite intensive aircraft are not fully known as they are still near the beginning of their design life, although it is clear that they are susceptible to internal impact damage, not visible at the surface. Yet current NDI of composites in production and service is still largely manual with low area coverage and NDI images are difficult to interpret for any technique because of macroscopic structural anisotropy. To address these problems the project proposes two key NDI innovations: (1) Up to 100% volume NDI coverage using gantry deployed, CAD controlled robotics so that inspection records at any point can be accurately compared at successive maintenance downtime intervals to allow health diagnostics and progostics. (2) A step increase in current detection probability for composite defects implemented through an inference engine performing similarity analysis on spatial and temporal changes in images with coherent noise removal performed by Bayesian separation. The innovations, validated using ultrasonic NDI, are applicable to any type of NDI sensor.