When am (additive manufacturing) meets ae (acoustic emission) and AI (artificial intelligence)


Acoustic Emission (AE) is an effective method to monitor and control the quality in different technical processes and phenomena, including tribology and fracture mechanics. However, in highly dynamic processes such as Laser Additive Manufacturing (LAM) of metal, the processing of AE signals is very burdensome. At the same time, artificial intelligence (AI) has been considered as a new and powerful tool to overcome the complexity of the large data processing with a reasonable computational time. In this contribution, we have summarized our prior works. To start with, we demonstrated that the combination of AE with state-of-the-art signal processing including AI makes it possible to differentiate several process conditions, workpiece quality and materials. Then, we present some limitations when changing process parameters and materials. Finally, we also introduce alternative AI methods to reduce the amount of data needed to train the AI algorithms as well as transfer the knowledge from one material to another one. This will give the reader an overview of the advances of monitoring the LAM process combining AE with AI techniques to make a significant step forward in in situ and real-time process monitoring and quality control. In particular,
