Multisensor Data Fusion for Additive Manufacturing Process Control

Achieving cutting-edge mechanical properties of metal parts realized by additive manufacturing (AM) demands articulated process control strategies, due to the multitude of physical phenomena involved in this kind of manufacturing processes. Complexity is even higher for what concerns the direct energy deposition (DED) technique, which offers much more potential flexibility and efficiency with respect to other metal AM technologies, at the cost of more difficult process control. The present work presents a multisensor approach able to combine online signals, collected while monitoring the deposition process, and data coming from offline inspection devices, during the built part quality check phase. This data fusion approach constitutes the foundation for the process modeling phase and, consequently, for the implementation of an intelligent control strategy that would act online by adjusting the machine process parameters chasing part dimensional, mechanical, and quality targets. The benefits of the proposed solution are assessed through a dedicated experimental campaign on a DED machine.