Deep learning was applied in sorting nanoindentation-induced acoustic emission events. The acoustic emission events were triggered by a plasticity onset and dislocations phenomena, observed on the electropolished W (100) sample during nanoindentation tests. The acoustic signal was recorded by a specialized sensor integrated into the nanoindenter tip. The signal was conditioned using analog/digital electronics and post-processed by the advanced signal processing routines that include entropy filtering, and Continuous Wavelet Transforms (CWT). Pseudo time-frequency domain plots were constructed by representing/plotting CWT coefficients in those two domains and creating topography maps. This arrangement presented AE event data in a commonly utilized graphic picture format, jpeg. The deep learning technology originally developed for generic image recognition, which operates on 224 × 224 × 3 sized jpeg images, was deployed for sorting out acoustic events. The GoogLeNet deep learning neural network was trained on predefined classifiers and then deployed on the raw acoustic signal data sets. The proposed deep learning acoustic emission event sorting methodology successfully differentiated W (100) plasticity onset from other types of nanoscale contact acoustic interactions.

深度学习被应用于对纳米压痕引起的声发射事件进行分类。在纳米压痕测试过程中，在电抛光的W（100）样品上观察到，可塑性的开始和位错现象触发了声发射事件。声音信号由集成在纳米压头尖端中的专用传感器记录。使用模拟/数字电子设备对信号进行调节，并通过包括熵滤波和连续小波变换（CWT）在内的高级信号处理例程进行后处理。通过在这两个域中表示/绘制CWT系数并创建地形图来构造伪时频域图。这种安排以一种常用的图形图片格式jpeg呈现AE事件数据。最初为通用图像识别而开发的深度学习技术可用于处理224×224×3尺寸的jpeg图像，已被用于整理声音事件。GoogLeNet深度学习神经网络在预定义的分类器上进行了训练，然后部署在原始声学信号数据集上。拟议的深度学习声发射事件分类方法成功地将W（100）可塑性发作与其他类型的纳米级接触声相互作用区分开来。