While resin 3D printers are seeing growing adoption in both manufacturing and personal
fabrication settings, detecting print failures in real time remains challenging. Object-detection
neural networks have shown benefits in a variety of extrusion-based 3D printing methods. Here,
we extend such work to resin printing using a physics-informed machine learning data generation
pipeline. Our approach leverages our models of the fluid dynamics of the printing process at
every slice, in order to synthetically generate a library of print defects. We show such an
approach is capable of providing data sufficiently resembling real-world failures to fine-tune a
pre-trained custom defect detection neural network that can alert users of failure in real-time.
Finally, to allow novice users to take advantage of our simulation platform, we integrate our tool
into an interactive augmented reality interface, which displays simulation predictions to provide
guidance on design and machine parameters prior to printing.Mechanical Engineerin
