  Early detection and correction of defects are critical in additive
manufacturing (AM) to avoid build failures. In this paper, we present a
multisensor fusion-based digital twin for in-situ quality monitoring and defect
correction in a robotic laser direct energy deposition process. Multisensor
fusion sources consist of an acoustic sensor, an infrared thermal camera, a
coaxial vision camera, and a laser line scanner. The key novelty and
contribution of this work are to develop a spatiotemporal data fusion method
that synchronizes and registers the multisensor features within the part's 3D
volume. The fused dataset can be used to predict location-specific quality
using machine learning. On-the-fly identification of regions requiring material
addition or removal is feasible. Robot toolpath and auto-tuned process
parameters are generated for defecting correction. In contrast to traditional
single-sensor-based monitoring, multisensor fusion allows for a more in-depth
understanding of underlying process physics, such as pore formation and
laser-material interactions. The proposed methods pave the way for
self-adaptation AM with higher efficiency, less waste, and cleaner production.
