This work provides a solution to the challenge of small amounts of training data in Non-Destructive Ultrasonic Testing for composite components. It was demonstrated that direct simulation alone is ineffective at producing training data that was representative of the experimental domain due to poor noise reconstruction. Therefore, four unique synthetic data generation methods were proposed which use semi-analytical simulated data as a foundation. Each method was evaluated on its classification performance of real experimental images when trained on a Convolutional Neural Network which underwent hyperparameter optimization using a genetic algorithm. The first method introduced task specific modifications to CycleGAN, to learn the mapping from physics-based simulations of defect indications to experimental indications in resulting ultrasound images. The second method was based on combining real experimental defect free images with simulated defect responses. The final two methods fully simulated the noise responses at an image and signal level respectively. The purely simulated data produced a mean classification F1 score of 0.394. However, when trained on the new synthetic datasets, a significant improvement in classification performance on experimental data was realized, with mean classification F1 scores of 0.843, 0.688, 0.629, and 0.738 for the respective approaches.

翻译：本研究针对复合材料构件非破坏性超声检测中训练数据量不足的挑战提供了解决方案。实验表明，由于噪声重构效果不佳，直接使用仿真方法无法生成能表征实验域特征的训练数据。为此，本文提出了四种以半解析仿真数据为基础的独特合成数据生成方法。每种方法在卷积神经网络（经遗传算法进行超参数优化）训练后，均基于其对真实实验图像的分类性能进行评估。第一种方法对CycleGAN引入任务特异性改进，学习将基于物理仿真的缺陷指示映射到超声图像中的实验指示。第二种方法结合了真实实验的无缺陷图像与仿真的缺陷响应。最后两种方法分别从图像级和信号级完全模拟噪声响应。纯仿真数据生成的分类F1平均分数为0.394。然而，基于新合成的数据集训练后，实验数据上的分类性能获得显著提升，四种方法对应的分类F1平均分数分别达到0.843、0.688、0.629和0.738。