Currently, the laser powder bed fusion (L-PBF) process cannot offer a reproducible and predefined quality of the processed parts. Recent research on process monitoring focuses strongly on integrated optical measurement technology. Besides optical sensors, acoustic sensors also seem promising. Previous studies have shown the potential of analyzing structure-borne and air-borne acoustic emissions in laser welding. Only a few works evaluate the potential that lies in the usage during the L-PBF process.

This work shows how the approach to structure-borne acoustic process monitoring can be elaborated by correlating acoustic signals to statistical values indicating part quality. Density measurements according to Archimedes’ principle are used to label the layer-based acoustic data and to measure the quality. The data set is then treated as a classification problem while investigating the applicability of existing artificial neural network algorithms to match acoustic data with density measurements. Furthermore, this work investigates the transferability of the approach to more complex specimens.

当前，激光粉末床熔合（L-PBF）工艺无法提供加工零件的可再现和预定义的质量。关于过程监控的最新研究主要集中在集成光学测量技术上。除了光学传感器，声学传感器似乎也很有前途。先前的研究显示了分析激光焊接中的结构声和空气声发射的潜力。仅少数作品评估了L-PBF过程中使用的潜力。

这项工作表明，如何通过将声学信号与指示零件质量的统计值相关联，来详细构造固体声学过程监控方法。根据阿基米德原理进行的密度测量用于标记基于层的声学数据并测量质量。然后，在研究现有人工神经网络算法将声学数据与密度测量值进行匹配的适用性时，将数据集视为分类问题。此外，这项工作研究了该方法对更复杂标本的可移植性。