Previous research (Li et al., Understanding the disharmony between dropout and batch normalization by variance shift. CoRR abs/1801.05134 (2018). http://arxiv.org/abs/1801.05134arXiv:1801.05134) has shown the plausibility of using a modern deep convolutional neural network to detect flaws from phased-array ultrasonic data. This brings the repeatability and effectiveness of automated systems to complex ultrasonic signal evaluation, previously done exclusively by human inspectors. The major breakthrough was to use virtual flaws to generate ample flaw data for the teaching of the algorithm. This enabled the use of raw ultrasonic scan data for detection and to leverage some of the approaches used in machine learning for image recognition. Unlike traditional image recognition, training data for ultrasonic inspection is scarce. While virtual flaws allow us to broaden the data considerably, original flaws with proper flaw-size distribution are still required. This is of course the same for training human inspectors. The training of human inspectors is usually done with easily manufacturable flaws such as side-drilled holes and EDM notches. While the difference between these easily manufactured artificial flaws and real flaws is obvious, human inspectors still manage to train with them and perform well in real inspection scenarios. In the present work, we use a modern, deep convolutional neural network to detect flaws from phased-array ultrasonic data and compare the results achieved from different training data obtained from various artificial flaws. The model demonstrated good generalization capability toward flaw sizes larger than the original training data, and the effect of the minimum flaw size in the data set affects the \(a_{90/95}\) value. This work also demonstrates how different artificial flaws, solidification cracks, EDM notch and simple simulated flaws generalize differently.

先前的研究（Li等人，通过方差移位了解辍学和批次归一化之间的不和谐.CoRR abs / 1801.05134（2018）。http://arxiv.org/abs/1801.05134arXiv:1801.05134）显示了使用现代深层卷积神经网络来检测相控阵超声数据中的缺陷。这将自动化系统的可重复性和有效性带到了复杂的超声波信号评估中，而超声波评估以前是专门由检查人员完成的。主要的突破是使用虚拟缺陷生成了足够的缺陷数据，用于算法的教学。这使得可以使用原始超声扫描数据进行检测，并可以利用机器学习中用于图像识别的某些方法。与传统的图像识别不同，用于超声检查的训练数据很少。尽管虚拟缺陷使我们可以大大扩展数据，但仍需要具有适当缺陷大小分布的原始缺陷。对于培训人类检查员而言，当然是相同的。对人员检查员的培训通常带有容易制造的缺陷，例如侧面钻孔和EDM缺口。尽管这些容易制造的人工缺陷和真实缺陷之间的区别是显而易见的，但人类检查人员仍设法对其进行培训，并在真实检查场景中表现良好。在当前工作中，我们使用现代的深度卷积神经网络从相控阵超声数据中检测缺陷，并比较从各种人工缺陷获得的不同训练数据中获得的结果。该模型展示了对大于原始训练数据的缺陷大小具有良好的泛化能力，\（a_ {90/95} \）值。这项工作还演示了不同的人工缺陷，凝固裂纹，EDM缺口和简单的模拟缺陷如何不同地概括。