Key Laboratory of Nondestructive Test of Ministry of Education, Nanchang Hangkong University, Nanchang 330063, China.
School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China.
Sensors (Basel). 2022 Apr 24;22(9):3274. doi: 10.3390/s22093274.
Because of the high cost of experimental data acquisition, the limited size of the sample set available when conducting tissue structure ultrasound evaluation can cause the evaluation model to have low accuracy. To address such a small-sample problem, the sample set size can be expanded by using virtual samples. In this study, an ultrasound evaluation method for the primary α phase grain size based on the generation of virtual samples by a generative adversarial network (GAN) was developed. TC25 titanium alloy forgings were treated as the research object. Virtual samples were generated by the GAN with a fully connected network of different sizes used as the generator and discriminator. A virtual sample screening mechanism was constructed to obtain the virtual sample set, taking the optimization rate as the validity criterion. Moreover, an ultrasound evaluation optimization problem was constructed with accuracy as the target. It was solved by using support vector machine regression to obtain the final ultrasound evaluation model. A benchmark function was adopted to verify the effectiveness of the method, and a series of experiments and comparison experiments were performed on the ultrasound evaluation model using test samples. The results show that the learning accuracy of the original small samples can be increased by effective virtual samples. The ultrasound evaluation model built based on the proposed method has a higher accuracy and better stability than other models.
由于实验数据获取成本高，在进行组织结构超声评估时，可用样本集的规模有限，这可能导致评估模型的准确性较低。为了解决小样本问题，可以通过生成虚拟样本来扩展样本集。本研究提出了一种基于生成对抗网络（GAN）生成虚拟样本的基于初级α相晶粒尺寸的超声评估方法。以 TC25 钛合金锻件为研究对象，使用全连接网络作为生成器和判别器的 GAN 生成虚拟样本。构建了一个虚拟样本筛选机制，以获得虚拟样本集，以优化率作为有效性标准。此外，构建了一个以精度为目标的超声评价优化问题，通过支持向量机回归进行求解，得到最终的超声评价模型。采用基准函数验证了该方法的有效性，并对测试样本进行了一系列超声评价模型的实验和对比实验。结果表明，通过有效的虚拟样本可以提高原始小样本的学习精度。基于所提出方法构建的超声评价模型具有更高的准确性和更好的稳定性。
