This study aims to relate the time-frequency patterns of acoustic emission (AE) and other multi-modal sensor data collected in a hybrid directed energy deposition (DED) process to the pore formations at high spatial (0.5 mm) and time (< 1ms) resolutions. Adapting an explainable AI method in LIME (Local Interpretable Model-Agnostic Explanations), certain high-frequency waveform signatures of AE are to be attributed to two major pathways for pore formation in a DED process, namely, spatter events and insufficient fusion between adjacent printing tracks from low heat input. This approach opens an exciting possibility to predict, in real-time, the presence of a pore in every voxel (0.5 mm in size) as they are printed, a major leap forward compared to prior efforts. Synchronized multimodal sensor data including force, AE, vibration and temperature were gathered while an SS316L material sample was printed and subsequently machined. A deep convolution neural network classifier was used to identify the presence of pores on a voxel surface based on time-frequency patterns (spectrograms) of the sensor data collected during the process chain. The results suggest signals collected during DED were more sensitive compared to those from machining for detecting porosity in voxels (classification test accuracy of 87%). The underlying explanations drawn from LIME analysis suggests that energy captured in high frequency AE waveforms are 33% lower for porous voxels indicating a relatively lower laser-material interaction in the melt pool, and hence insufficient fusion and poor overlap between adjacent printing tracks. The porous voxels for which spatter events were prevalent during printing had about 27% higher energy contents in the high frequency AE band compared to other porous voxels. These signatures from AE signal can further the understanding of pore formation from spatter and insufficient fusion.

翻译：本研究旨在将混合定向能量沉积（DED）过程中采集的声发射（AE）及其他多模态传感器数据的时间-频率模式，与高空间分辨率（0.5 mm）和时间分辨率（<1 ms）下的孔隙形成相关联。通过采用可解释人工智能方法LIME（局部可解释模型无关解释），将AE的特定高频波形特征归因于DED过程中孔隙形成的两种主要途径，即飞溅事件和低热输入导致的相邻打印轨迹间熔合不足。该方法为实时预测每个打印体素（尺寸为0.5 mm）中是否存在孔隙开辟了令人兴奋的可能性，相比先前研究实现了重大飞跃。在打印SS316L材料样本并随后进行机械加工的过程中，同步采集了包括力、AE、振动和温度在内的多模态传感器数据。采用深度卷积神经网络分类器，基于工艺链中采集的传感器数据的时间-频率模式（频谱图）识别体素表面是否存在孔隙。结果表明，与机械加工阶段相比，DED过程中采集的信号对检测体素孔隙更为敏感（分类测试准确率达87%）。基于LIME分析得出的解释表明，多孔体素的高频AE波形能量捕获量低33%，表明熔池中激光-材料相互作用相对较弱，从而导致相邻打印轨迹间熔合不足及重叠较差。在打印过程中飞溅事件频繁的多孔体素，其高频AE频段能量含量比其他多孔体素高约27%。这些AE信号特征可进一步加深对飞溅和熔合不足所致孔隙形成的理解。