3D object quality prediction for Metal Jet Printer with Multimodal
  thermal encoder
Rachel Chen Wenjia Zheng Sandeep Jalui Pavan Suri Jun Zeng
With the advancements in 3D printing technologies, it is extremely important
that the quality of 3D printed objects, and dimensional accuracies should meet
the customer's specifications. Various factors during metal printing affect the
printed parts' quality, including the power quality, the printing stage
parameters, the print part's location inside the print bed, the curing stage
parameters, and the metal sintering process. With the large data gathered from
HP's MetJet printing process, AI techniques can be used to analyze, learn, and
effectively infer the printed part quality metrics, as well as assist in
improving the print yield. In-situ thermal sensing data captured by
printer-installed thermal sensors contains the part thermal signature of fusing
layers. Such part thermal signature contains a convoluted impact from various
factors. In this paper, we use a multimodal thermal encoder network to fuse
data of a different nature including the video data vectorized printer control
data, and exact part thermal signatures with a trained encoder-decoder module.
We explored the data fusing techniques and stages for data fusing, the
optimized end-to-end model architecture indicates an improved part quality
prediction accuracy.