[Data] Optimizing In-situ Monitoring for Laser Powder Bed Fusion Process: Deciphering Acoustic Emission and Sensor Sensitivity with Explainable Machine Learning

Intentional variations in Metal-Laser Powder Bed Process (L-PBF) spaces were introduced by employing two distinct distributions of 316L stainless steel powder, characterized by particle sizes both greater than 45 μm and less than 45 μm. These powders underwent processing with two different sets of laser parameters, resulting in the generation of four datasets (D1, D2, D3, D4). These datasets encapsulate airborne Acoustic Emissions (AE) arising from diverse build qualities, including Lack of Fusion (LoF) pores, conduction mode, and keyhole formations. The experiments were conducted utilizing a Sisma MYSINT 100 commercial LPBF printer and an airborne Acoustic Emission (AE) sensor system boasting a flat frequency response ranging from 0 to 150 kHz. Validation of the ground truths for the three laser regimes across the four datasets, representing distinct process spaces, was accomplished through the confirmation of cross-sectional images. In the fabrication of a cube using a powder bed and laser, data acquisition from an AE sensor commenced when the optical intensity reached a threshold of 0.5 V for each scan length. The photodiode trigger gain was adjusted to saturate at 5 V, and the subsequent continuous-time window, where the optical signal remained at 5 V for 12.5 ms, was calculated and segmented to generate the dataset. Irrespective of the specific regime (Lack of Fusion, Conduction, and Keyhole) or the cube being fabricated (with two powder distributions), the signals obtained during this process were segmented into a 12.5 ms window comprising 5000 data points. Each dataset is accompanied by two files – one for raw data and the other for ground truth labels. To mitigate noise, an offline low-pass Butterworth filter with a 150 kHz cut-off frequency was applied, aligning with the frequency response specification of the AE sensor. The total number of AE windows per dataset is approximately 7500 raw AE signals.本数据集通过采用两种粒径分布各异的316L不锈钢粉末（粒径分别大于45μm与小于45μm），构建了金属激光粉末床熔融（Laser Powder Bed Fusion, L-PBF）工艺区间的可控变量。将上述两种粉末分别配合两组不同的激光工艺参数进行加工，最终得到四个数据集（D1、D2、D3、D4）。各数据集均涵盖由不同成形质量所产生的空气中传播的声发射（Acoustic Emission, AE）信号，成形质量类型包括未熔合（Lack of Fusion, LoF）孔隙、传导模式熔池以及锁孔模式熔池。实验依托Sisma MYSINT 100商用L-PBF打印机展开，并使用一套频率响应平坦范围为0至150kHz的空气中声发射传感系统完成信号采集。针对四个数据集所覆盖的三种不同激光工艺区间，其真值标签的验证均通过横截面成像分析完成。在采用粉末床与激光制备立方体试样的过程中，当每段扫描路径的光强达到0.5V阈值时，声发射传感器即启动数据采集。将光电二极管的触发增益调节至5V饱和状态，随后对光强保持5V达12.5ms的连续时间窗口进行计算与分段，以此生成目标数据集。无论采用何种工艺区间（未熔合、传导模式、锁孔模式）或是使用哪种粉末分布制备试样，该过程中采集到的信号均被分割为包含5000个数据点的12.5ms窗口。每个数据集均附带两个文件，分别存储原始数据与真值标签。为抑制噪声干扰，本数据集采用截止频率为150kHz的离线巴特沃斯低通滤波器进行预处理，该参数与声发射传感器的频率响应规格相匹配。每个数据集约包含7500个原始声发射信号窗口。